Enterprise Sales Engineer Jobs in Georgia
Enterprise Sales Engineer jobs in Georgia are highly active, concentrated in technology, cybersecurity, cloud infrastructure, and manufacturing software sectors, with openings at every level from early-career through principal and staff engineer. Atlanta anchors the bulk of hiring, with meaningful activity also in Alpharetta and Savannah, where established employers like NCR Voyix, Palo Alto Networks, and IBM maintain significant Georgia footprints. Cloud platforms, SaaS sales cycles, and industrial automation are the most in-demand specialties for this role in the state. Find a role that fits below and apply directly.
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Wolters Kluwer is seeking a hands-on Principal AI Solutions Engineer in our Architecture & Engineering Services (AES) Team to architect, build, and deploy enterprise-scale AI solutions on our next-generation AI platform. Working within our Engineering team and collaborating closely with customers, Cloud Operations, Security, Compliance, and business stakeholders, this principal engineer will drive AI-powered transformation initiatives across the enterprise.
Our Principal AI Solutions Engineer will lead the development of intelligent, cloud-native, agentic AI solutions that operationalize decision-making and process automation across the organization. The role requires deep expertise in designing AI agents, composing orchestrations, engineering reusable skills, and building extensible plugins that solve complex business problems. Beyond architecting AI systems, this engineer will demonstrate thought leadership in translating business processes into AI-driven workflows and will play a key role in evolving our AI platform roadmap.
Key Responsibilities:
- Translate business processes and operational workflows into AI use cases solved via agents, orchestrations, skills, and plugins on our platform
- Lead the design and delivery of end-to-end AI solutions that integrate process engineering principles with business logic
- Architect agentic systems that combine intent-driven decision-making, dynamic authorization, and controlled execution
- Accelerate AI adoption across the organization by democratizing access to AI capabilities, empowering business units and engineering teams to build, deploy, and operationalize intelligent workflows without deep AI expertise
- Mentor the engineering team in AI solution design patterns, governance frameworks, and best practices
- Shape the AI platform roadmap based on customer needs, operational requirements, and emerging patterns
- Collaborate with stakeholders to define AI governance, compliance, and risk controls for enterprise deployments
- Build and operationalize reusable, composable skills, orchestrations, and patterns that amplify team velocity and reduce time-to-value for downstream adopters
This role demands an engineer who understands both the technical depth of AI systems and the business context they operate within—and who is committed to scaling AI impact across the enterprise.
What YOU’ll have
Required Qualifications & Mindset
Core Motivation & Approach:
- Driven to transform enterprise operations through AI agents and intelligent automation
- Passionate about democratizing AI—making advanced capabilities accessible to business units and non-expert engineering teams
- Motivated by solving complex, real-world process problems with scalable, resilient solutions
- "Can-do" attitude; comfortable designing and shipping solutions under pressure and ambiguity
Technical Foundation:
- Strong grasp of cloud platforms and hybrid environments; comfortable architecting for Azure, AWS, or GCP
- Deep understanding of distributed systems, microservices architecture, and cloud-native patterns
- Proven experience with high-level programming languages (Python preferred; Java/C# acceptable)
- Strong fundamentals: OS concepts, storage solutions, networking, and infrastructure principles
AI/LLM & Agentic Systems:
- Proven track record building production LLM applications and agentic systems
- Deep expertise designing and orchestrating AI agents that make decisions, take actions, and execute workflows autonomously
- Proficiency in Python; strong experience with AI frameworks and SDKs (LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, or equivalent)
- Understanding of intent-driven decision-making, dynamic authorization models, and controlled execution patterns
- Experience building reusable, composable AI components (skills, orchestrations, plugins) that democratize AI across teams
Process Engineering & Business Translation:
- Ability to translate business processes and operational workflows into AI-solvable use cases
- Skilled at decomposing complex workflows into orchestrations, skills, and plugins that work together
- Comfort with process automation platforms, workflow engines, and integration patterns
- Experience collaborating with business stakeholders to identify automation opportunities and design intelligent workflows
API, Integration & Architecture:
- Expert in designing and consuming APIs; strong background with async patterns and event-driven architectures
- Experience designing scalable integration patterns that connect AI solutions to enterprise systems
- Comfortable with hybrid and multi-cloud integration challenges
Testing, Quality & Governance:
- Strong background in testing AI/ML systems (unit, integration, evaluation); familiar with model benchmarking and validation frameworks
- Deep experience designing AI governance frameworks, policy enforcement layers, and risk classification models
- Skilled at building audit trails, compliance controls, and observability for regulated industries
- Committed to quality; driven to continuously improve solutions, processes, and platform capabilities
Cloud & Data Architecture:
- Deep experience with Azure services (Azure OpenAI, Azure AI Services, Cosmos DB, Functions, Azure Cognitive Search) or equivalent on AWS/GCP
- Understanding of data pipelines, vector databases (Azure Cognitive Search), and knowledge graph construction
- Experience designing data governance and knowledge management for AI systems
Collaboration & Leadership:
- Cross-functional mindset; comfortable working with business stakeholders, customers, security, and compliance teams
- Skilled at translating between business problems and technical solutions
- Mentoring mindset; driven to grow the engineering team's AI capabilities and design patterns
- Thought leadership in AI solution architecture, governance, and best practices
Execution & Continuous Learning:
- Not afraid to prototype, test, and ship quickly; able to hack scalable solutions under pressure
- Strong problem-solving skills; driven to fix bugs and continuously improve existing solutions
- Comfortable with ambiguity and emerging AI patterns; eager to pioneer new approaches
- Resilience and "can-do" attitude toward technical challenges and organizational complexity
- RESPONSIBILITIES Organizational AI Maturity & Knowledge Transfer: Design and execute a strategic roadmap for scaling AI adoption across the enterprise—moving from pilots to production at scale Build organizational capability by creating reusable patterns, reference architectures, and best practices that multiply team velocity Mentor and upskill engineering teams in agentic systems, orchestrations, and AI solution design—transforming AI from specialist domain to accessible practice Establish AI solution maturity models that help business units and engineering teams assess readiness and accelerate time-to-value Democratize AI across the organization by building and training teams on composable skills, orchestrations, and plugins that abstract complexity Platform-Led Training & Education: Leverage the AI platform itself as a learning and experimentation environment—enabling teams to build, test, and deploy solutions in a safe, governed sandbox Design hands-on workshops, labs, and playbooks that guide teams from business problems to deployed AI solutions Create and maintain solution templates and reference implementations that serve as both learning tools and accelerators for new projects Document and evangelize emerging patterns, governance frameworks, and best practices across the organization
- Strategic Research & Technology Innovation:
- Research, propose, and implement emerging AI technologies and paradigms (agentic orchestration, safety patterns, cost optimization) that improve platform resilience, scalability, and economics Conduct POCs and evaluate new LLM models, frameworks, and cloud services (Azure OpenAI, newer models, optimization techniques) that advance platform capabilities Improve engineering velocity metrics, solution quality benchmarks, and AI governance maturity—quantifying the business impact of platform investments
- Solution Architecture & Governance:
- Produce engineering design artifacts, functional and design specifications that translate business workflows into agentic solutions Implement and advocate for AI safety patterns (output validation, escalation gates, controlled execution, rollback mechanisms) across all solutions Design AI governance frameworks aligned with NIST AI RMF and ISO/IEC 42001 for regulated industries (healthcare, financial services, legal tech) Ensure compliance with SOC 2, ISO 27001, HIPAA, and other regulatory requirements in all AI deployments Agentic Systems Development:
- Architect and deliver agentic orchestration systems (multi-agent systems, tool use, long-running workflows, intent-driven decision-making) Implement AI safety and governance patterns that enable responsible, auditable agent autonomy Optimize production LLM applications for cost and performance (prompt caching, model quantization, intelligent batching) Design solutions for regulated industries that meet stringent compliance and audit requirements Platform Evolution & Thought Leadership:
- Participate in strategic planning sessions to shape the AI platform roadmap based on emerging needs, customer feedback, and industry trends Identify and advocate for new processes, methodologies, and governance paradigms that make AI solutions more resilient, economical, and trustworthy Contribute to industry conversations on AI safety, governance, and responsible deployment—potentially through open-source contributions, research, or speaking engagements Pilot new technologies, validate cloud provider solutions, and propose innovations that strengthen platform capabilities and organizational AI maturity
- EXPERIENCE & SKILLS REQUIRED Organizational AI Maturity & Knowledge Transfer: Design and execute a strategic roadmap for scaling AI adoption across the enterprise—moving from pilots to production at scale
- Build organizational capability by creating reusable patterns, reference architectures, and best practices that multiply team velocity
- Mentor and upskill engineering teams in agentic systems, orchestrations, and AI solution design—transforming AI from specialist domain to accessible practice
- Establish AI solution maturity models that help business units and engineering teams assess readiness and accelerate time-to-value
- Democratize AI across the organization by building and training teams on composable skills, orchestrations, and plugins that abstract complexity
- Platform-Led Training & Education: Leverage the AI platform itself as a learning and experimentation environment—enabling teams to build, test, and deploy solutions in a safe, governed sandboxDesign hands-on workshops, labs, and playbooks that guide teams from business problem to deployed AI solutionsCreate and maintain solution templates and reference implementations that serve as both learning tools and accelerators for new projects
- Document and evangelize emerging patterns, governance frameworks, and best practices across the organization undefined
- Strategic Research & Technology Innovation: Research, propose, and implement emerging AI technologies and paradigms (agentic orchestration, safety patterns, cost optimization) that improve platform resilience, scalability, and economics
- Conduct POCs and evaluate new LLM models, frameworks, and cloud services (Azure OpenAI, newer models, optimization techniques) that advance platform capabilities
- Improve engineering velocity metrics, solution quality benchmarks, and AI governance maturity—quantifying the business impact of platform investments undefined
- Solution Architecture & Governance: Produce engineering design artifacts, functional and design specifications that translate business workflows into agentic solutions
- Implement and advocate for AI safety patterns (output validation, escalation gates, controlled execution, rollback mechanisms) across all solutionsDesign AI governance frameworks aligned with NIST AI RMF and ISO/IEC 42001 for regulated industries (healthcare, financial services, legal tech)Ensure compliance with SOC 2, ISO 27001, HIPAA, and other regulatory requirements in all AI deployments undefined
- Agentic Systems Development: Architect and deliver agentic orchestration systems (multi-agent systems, tool use, long-running workflows, intent-driven decision-making)Implement AI safety and governance patterns that enable responsible, auditable agent autonomy
- Optimize production LLM applications for cost and performance (prompt caching, model quantization, intelligent batching)Design solutions for regulated industries that meet stringent compliance and audit requirements undefined Platform Evolution & Thought Leadership: Participate in strategic planning sessions to shape the AI platform roadmap based on emerging needs, customer feedback, and industry trends
- Identify and advocate for new processes, methodologies, and governance paradigms that make AI solutions more resilient, economical, and trustworthy Contribute to industry conversations on AI safety, governance, and responsible deployment—potentially through open-source contributions, research, or speaking engagements Pilot new technologies, validate cloud provider solutions, and propose innovations that strengthen platform capabilities and organizational AI maturity
- EDUCATION: Bachelor's degree in Computer Science, Information Systems, or a related technical field (required) Master's degree in Computer Science, Engineering, Business Administration, or related field (strongly preferred) PhD in Computer Science, Machine Learning, or related discipline (a plus)
Our Interview Practices
To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.
Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.
Compensation:
This role is eligible for Bonus.
Compensation range listed is based on primary location of the position. Actual base salary offer is influenced by a wide array of factors including but not limited to skills, experience and actual hiring location. Your recruiter can share more information about the specific offer for the job location during the hiring process.
Additional Information:
Wolters Kluwer offers a wide variety of competitive benefits and programs to help meet your needs and balance your work and personal life, including but not limited to: Medical, Dental, & Vision Plans, 401(k), FSA/HSA, Commuter Benefits, Tuition Assistance Plan, Vacation and Sick Time, and Paid Parental Leave. Full details of our benefits are available upon request.
Wolters Kluwer is seeking a hands-on Principal AI Solutions Engineer in our Architecture & Engineering Services (AES) Team to architect, build, and deploy enterprise-scale AI solutions on our next-generation AI platform. Working within our Engineering team and collaborating closely with customers, Cloud Operations, Security, Compliance, and business stakeholders, this principal engineer will drive AI-powered transformation initiatives across the enterprise.
Our Principal AI Solutions Engineer will lead the development of intelligent, cloud-native, agentic AI solutions that operationalize decision-making and process automation across the organization. The role requires deep expertise in designing AI agents, composing orchestrations, engineering reusable skills, and building extensible plugins that solve complex business problems. Beyond architecting AI systems, this engineer will demonstrate thought leadership in translating business processes into AI-driven workflows and will play a key role in evolving our AI platform roadmap.
Key Responsibilities:
- Translate business processes and operational workflows into AI use cases solved via agents, orchestrations, skills, and plugins on our platform
- Lead the design and delivery of end-to-end AI solutions that integrate process engineering principles with business logic
- Architect agentic systems that combine intent-driven decision-making, dynamic authorization, and controlled execution
- Accelerate AI adoption across the organization by democratizing access to AI capabilities, empowering business units and engineering teams to build, deploy, and operationalize intelligent workflows without deep AI expertise
- Mentor the engineering team in AI solution design patterns, governance frameworks, and best practices
- Shape the AI platform roadmap based on customer needs, operational requirements, and emerging patterns
- Collaborate with stakeholders to define AI governance, compliance, and risk controls for enterprise deployments
- Build and operationalize reusable, composable skills, orchestrations, and patterns that amplify team velocity and reduce time-to-value for downstream adopters
This role demands an engineer who understands both the technical depth of AI systems and the business context they operate within—and who is committed to scaling AI impact across the enterprise.
What YOU’ll have
Required Qualifications & Mindset
Core Motivation & Approach:
- Driven to transform enterprise operations through AI agents and intelligent automation
- Passionate about democratizing AI—making advanced capabilities accessible to business units and non-expert engineering teams
- Motivated by solving complex, real-world process problems with scalable, resilient solutions
- "Can-do" attitude; comfortable designing and shipping solutions under pressure and ambiguity
Technical Foundation:
- Strong grasp of cloud platforms and hybrid environments; comfortable architecting for Azure, AWS, or GCP
- Deep understanding of distributed systems, microservices architecture, and cloud-native patterns
- Proven experience with high-level programming languages (Python preferred; Java/C# acceptable)
- Strong fundamentals: OS concepts, storage solutions, networking, and infrastructure principles
AI/LLM & Agentic Systems:
- Proven track record building production LLM applications and agentic systems
- Deep expertise designing and orchestrating AI agents that make decisions, take actions, and execute workflows autonomously
- Proficiency in Python; strong experience with AI frameworks and SDKs (LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, or equivalent)
- Understanding of intent-driven decision-making, dynamic authorization models, and controlled execution patterns
- Experience building reusable, composable AI components (skills, orchestrations, plugins) that democratize AI across teams
Process Engineering & Business Translation:
- Ability to translate business processes and operational workflows into AI-solvable use cases
- Skilled at decomposing complex workflows into orchestrations, skills, and plugins that work together
- Comfort with process automation platforms, workflow engines, and integration patterns
- Experience collaborating with business stakeholders to identify automation opportunities and design intelligent workflows
API, Integration & Architecture:
- Expert in designing and consuming APIs; strong background with async patterns and event-driven architectures
- Experience designing scalable integration patterns that connect AI solutions to enterprise systems
- Comfortable with hybrid and multi-cloud integration challenges
Testing, Quality & Governance:
- Strong background in testing AI/ML systems (unit, integration, evaluation); familiar with model benchmarking and validation frameworks
- Deep experience designing AI governance frameworks, policy enforcement layers, and risk classification models
- Skilled at building audit trails, compliance controls, and observability for regulated industries
- Committed to quality; driven to continuously improve solutions, processes, and platform capabilities
Cloud & Data Architecture:
- Deep experience with Azure services (Azure OpenAI, Azure AI Services, Cosmos DB, Functions, Azure Cognitive Search) or equivalent on AWS/GCP
- Understanding of data pipelines, vector databases (Azure Cognitive Search), and knowledge graph construction
- Experience designing data governance and knowledge management for AI systems
Collaboration & Leadership:
- Cross-functional mindset; comfortable working with business stakeholders, customers, security, and compliance teams
- Skilled at translating between business problems and technical solutions
- Mentoring mindset; driven to grow the engineering team's AI capabilities and design patterns
- Thought leadership in AI solution architecture, governance, and best practices
Execution & Continuous Learning:
- Not afraid to prototype, test, and ship quickly; able to hack scalable solutions under pressure
- Strong problem-solving skills; driven to fix bugs and continuously improve existing solutions
- Comfortable with ambiguity and emerging AI patterns; eager to pioneer new approaches
- Resilience and "can-do" attitude toward technical challenges and organizational complexity
- RESPONSIBILITIES Organizational AI Maturity & Knowledge Transfer: Design and execute a strategic roadmap for scaling AI adoption across the enterprise—moving from pilots to production at scale Build organizational capability by creating reusable patterns, reference architectures, and best practices that multiply team velocity Mentor and upskill engineering teams in agentic systems, orchestrations, and AI solution design—transforming AI from specialist domain to accessible practice Establish AI solution maturity models that help business units and engineering teams assess readiness and accelerate time-to-value Democratize AI across the organization by building and training teams on composable skills, orchestrations, and plugins that abstract complexity Platform-Led Training & Education: Leverage the AI platform itself as a learning and experimentation environment—enabling teams to build, test, and deploy solutions in a safe, governed sandbox Design hands-on workshops, labs, and playbooks that guide teams from business problems to deployed AI solutions Create and maintain solution templates and reference implementations that serve as both learning tools and accelerators for new projects Document and evangelize emerging patterns, governance frameworks, and best practices across the organization
- Strategic Research & Technology Innovation:
- Research, propose, and implement emerging AI technologies and paradigms (agentic orchestration, safety patterns, cost optimization) that improve platform resilience, scalability, and economics Conduct POCs and evaluate new LLM models, frameworks, and cloud services (Azure OpenAI, newer models, optimization techniques) that advance platform capabilities Improve engineering velocity metrics, solution quality benchmarks, and AI governance maturity—quantifying the business impact of platform investments
- Solution Architecture & Governance:
- Produce engineering design artifacts, functional and design specifications that translate business workflows into agentic solutions Implement and advocate for AI safety patterns (output validation, escalation gates, controlled execution, rollback mechanisms) across all solutions Design AI governance frameworks aligned with NIST AI RMF and ISO/IEC 42001 for regulated industries (healthcare, financial services, legal tech) Ensure compliance with SOC 2, ISO 27001, HIPAA, and other regulatory requirements in all AI deployments Agentic Systems Development:
- Architect and deliver agentic orchestration systems (multi-agent systems, tool use, long-running workflows, intent-driven decision-making) Implement AI safety and governance patterns that enable responsible, auditable agent autonomy Optimize production LLM applications for cost and performance (prompt caching, model quantization, intelligent batching) Design solutions for regulated industries that meet stringent compliance and audit requirements Platform Evolution & Thought Leadership:
- Participate in strategic planning sessions to shape the AI platform roadmap based on emerging needs, customer feedback, and industry trends Identify and advocate for new processes, methodologies, and governance paradigms that make AI solutions more resilient, economical, and trustworthy Contribute to industry conversations on AI safety, governance, and responsible deployment—potentially through open-source contributions, research, or speaking engagements Pilot new technologies, validate cloud provider solutions, and propose innovations that strengthen platform capabilities and organizational AI maturity
- EXPERIENCE & SKILLS REQUIRED Organizational AI Maturity & Knowledge Transfer: Design and execute a strategic roadmap for scaling AI adoption across the enterprise—moving from pilots to production at scale
- Build organizational capability by creating reusable patterns, reference architectures, and best practices that multiply team velocity
- Mentor and upskill engineering teams in agentic systems, orchestrations, and AI solution design—transforming AI from specialist domain to accessible practice
- Establish AI solution maturity models that help business units and engineering teams assess readiness and accelerate time-to-value
- Democratize AI across the organization by building and training teams on composable skills, orchestrations, and plugins that abstract complexity
- Platform-Led Training & Education: Leverage the AI platform itself as a learning and experimentation environment—enabling teams to build, test, and deploy solutions in a safe, governed sandboxDesign hands-on workshops, labs, and playbooks that guide teams from business problem to deployed AI solutionsCreate and maintain solution templates and reference implementations that serve as both learning tools and accelerators for new projects
- Document and evangelize emerging patterns, governance frameworks, and best practices across the organization undefined
- Strategic Research & Technology Innovation: Research, propose, and implement emerging AI technologies and paradigms (agentic orchestration, safety patterns, cost optimization) that improve platform resilience, scalability, and economics
- Conduct POCs and evaluate new LLM models, frameworks, and cloud services (Azure OpenAI, newer models, optimization techniques) that advance platform capabilities
- Improve engineering velocity metrics, solution quality benchmarks, and AI governance maturity—quantifying the business impact of platform investments undefined
- Solution Architecture & Governance: Produce engineering design artifacts, functional and design specifications that translate business workflows into agentic solutions
- Implement and advocate for AI safety patterns (output validation, escalation gates, controlled execution, rollback mechanisms) across all solutionsDesign AI governance frameworks aligned with NIST AI RMF and ISO/IEC 42001 for regulated industries (healthcare, financial services, legal tech)Ensure compliance with SOC 2, ISO 27001, HIPAA, and other regulatory requirements in all AI deployments undefined
- Agentic Systems Development: Architect and deliver agentic orchestration systems (multi-agent systems, tool use, long-running workflows, intent-driven decision-making)Implement AI safety and governance patterns that enable responsible, auditable agent autonomy
- Optimize production LLM applications for cost and performance (prompt caching, model quantization, intelligent batching)Design solutions for regulated industries that meet stringent compliance and audit requirements undefined Platform Evolution & Thought Leadership: Participate in strategic planning sessions to shape the AI platform roadmap based on emerging needs, customer feedback, and industry trends
- Identify and advocate for new processes, methodologies, and governance paradigms that make AI solutions more resilient, economical, and trustworthy Contribute to industry conversations on AI safety, governance, and responsible deployment—potentially through open-source contributions, research, or speaking engagements Pilot new technologies, validate cloud provider solutions, and propose innovations that strengthen platform capabilities and organizational AI maturity
- EDUCATION: Bachelor's degree in Computer Science, Information Systems, or a related technical field (required) Master's degree in Computer Science, Engineering, Business Administration, or related field (strongly preferred) PhD in Computer Science, Machine Learning, or related discipline (a plus)
Our Interview Practices
To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.
Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.
Compensation:
This role is eligible for Bonus.
Compensation range listed is based on primary location of the position. Actual base salary offer is influenced by a wide array of factors including but not limited to skills, experience and actual hiring location. Your recruiter can share more information about the specific offer for the job location during the hiring process.
Additional Information:
Wolters Kluwer offers a wide variety of competitive benefits and programs to help meet your needs and balance your work and personal life, including but not limited to: Medical, Dental, & Vision Plans, 401(k), FSA/HSA, Commuter Benefits, Tuition Assistance Plan, Vacation and Sick Time, and Paid Parental Leave. Full details of our benefits are available upon request.
See All 15 Enterprise Sales Engineer Jobs in Georgia
Find roles in Georgia that match your experience and apply in just a few clicks.
Find JobsEnterprise Sales Engineer Jobs by City in Georgia
Where Georgia roles are concentrated, by current openings.
Enterprise Sales Engineer Job Market in Georgia
A snapshot from current Georgia openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
- Healthcare & Medical Services
What Georgia Employers Look For
The qualifications that appear most often in enterprise sales engineer jobs across Georgia.
- Bachelor's degree in engineering, computer science, or a related technical field required
- Five or more years of enterprise software or technology sales engineering experience preferred
- Demonstrated ability to lead technical discovery and solution presentations for C-suite audiences
- Hands-on proficiency with CRM platforms such as Salesforce and enterprise integration tools
- Experience with cloud platforms including AWS, Azure, or Google Cloud in a pre-sales context
- Strong written and verbal communication skills for complex technical proposals and RFP responses
Enterprise Sales Engineer Jobs in Georgia: Frequently Asked Questions
How do you become a enterprise sales engineer in Georgia?
The most common path into an enterprise sales engineer role in Georgia starts with a bachelor's degree in engineering, computer science, or information systems, followed by experience in a technical role such as systems administration, software development, or IT consulting. Georgia does not require a state-issued license for this position. Most Georgia employers look for candidates who can bridge technical depth with sales acumen, so building experience in client-facing technical roles within Atlanta's technology corridor is a strong foundation.
How much do enterprise sales engineers make in Georgia?
Enterprise sales engineers in Georgia earn a median of about $98,840 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $72,580 for the lowest 10% to over $159,760 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire enterprise sales engineers in Georgia?
Employers hiring enterprise sales engineers in Georgia right now include American Express, Fanduel, and Greenberg Traurig, based on current listings on Migrate Mate as of November 2026. Georgia's concentration of technology headquarters and regional offices, particularly along the Alpharetta and Atlanta corridor, means many national and global enterprise software companies maintain active pre-sales teams in the state.
Which Georgia cities have the most enterprise sales engineer jobs?
Atlanta, Kennesaw, and Duluth have the most enterprise sales engineer openings in Georgia. Atlanta drives the majority of demand as the state's primary technology and corporate hub, home to the headquarters and regional offices of major cloud, fintech, and cybersecurity employers, while Alpharetta's dense concentration of technology firms and Savannah's growing logistics and industrial technology sector account for meaningful activity in those markets.
Are there remote enterprise sales engineer jobs in Georgia?
Yes, and more than most fields. About 100% of enterprise sales engineer openings tied to Georgia are remote or hybrid as of November 2026, reflecting how heavily the role relies on virtual demos, digital collaboration tools, and distributed sales team structures. The portions of the role most likely to remain fully remote are technical discovery calls, solution presentations, and proposal development, while on-site engagement with key accounts or executive stakeholders is common for senior positions.
How can I get hired as a enterprise sales engineer in Georgia with little or no experience?
The most realistic entry point is moving laterally from a technical support, solutions consultant, or inside sales role at a Georgia-based technology company, where you already interact with enterprise clients and product teams. Large Georgia employers in Alpharetta's technology corridor and Atlanta's Midtown and Buckhead office markets frequently bring on associate pre-sales engineers or technical sales development representatives as stepping-stone roles. Earning a vendor certification in AWS, Salesforce, or a major cybersecurity platform gives candidates without a formal pre-sales background a concrete credential that hiring managers in Georgia recognize.
Where can I find and apply to enterprise sales engineer jobs in Georgia?
You can find and apply to enterprise sales engineer jobs in Georgia on Migrate Mate, which lists current Georgia openings across Atlanta, Alpharetta, Savannah, and other markets in the state. Find roles that fit your experience and specialization and apply directly to the employers posting them.
See All 15 Enterprise Sales Engineer Jobs in Georgia
Find roles in Georgia that match your experience and apply in just a few clicks.
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