Senior Level Generative AI Analyst Jobs
Senior level generative ai analyst jobs put experienced professionals in charge of model strategy, end-to-end solution ownership, and the cross-functional teams driving deployment at scale. Openings are concentrated across Technology & Software, Retail, and Law & Legal Services, with a mix of on-site, remote, and hybrid settings, and employers like NVIDIA, Amazon, and Cullerton hiring at this level now.
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Showing 5 of 6+ Senior Level Generative AI Analyst jobs
INTRODUCTION
Cullerton Group has a new opportunity for a Solutions Architect 5 – Enterprise AI & Generative AI Architect in Peoria, Illinois, or Irving, Texas. The work will be performed 100% onsite, five days per week, with no remote or hybrid options. This is a 12-month position that can lead to permanent employment with our client. Compensation is $112/hr W-2 plus full benefits (vision, dental, health insurance, 401k, and holiday pay).
JOB SUMMARY
The Solutions Architect 5 will support the design and development of enterprise Artificial Intelligence (AI), Generative AI, and data-driven solutions across complex cloud and on-premises environments. This role focuses on collaborating with business stakeholders, enterprise architects, and technical development teams to identify business needs, define AI use cases, and translate requirements into scalable solution architectures. The architect will evaluate technologies, integration patterns, and architectural tradeoffs while providing technical guidance, reference implementations, and implementation recommendations. The ideal candidate will have extensive enterprise solution architecture experience, strong knowledge of modern AI technologies such as Retrieval-Augmented Generation (RAG) and agentic AI, and the ability to bridge business requirements with practical technical solutions.
KEY RESPONSIBILITIES
- Lead and participate in business discovery sessions, collaborating with stakeholders to identify objectives, pain points, functional requirements, constraints, and opportunities for enterprise AI and Generative AI solutions.
- Design and recommend scalable enterprise AI solution architectures, including RAG, vector search, enterprise search, prompt orchestration, agentic AI, multi-agent workflows, and model integration patterns.
- Evaluate cloud, on-premises, and hybrid architecture options, including APIs, microservices, containerized applications, distributed computing platforms, and enterprise identity/security integration.
- Provide architectural guidance to internal development teams, including implementation planning, solution feasibility analysis, technical tradeoff evaluations, reference implementations, and lightweight prototypes.
- Collaborate with enterprise architects, data scientists, machine learning engineers, application developers, and business stakeholders to create architecture documentation, reusable design patterns, technical recommendations, and implementation roadmaps.
BASIC QUALIFICATIONS
- Bachelor's degree in Computer Science, Information Technology, Software Engineering, Computer Engineering, or a closely related technical discipline.
- 10+ years of relevant professional experience in enterprise solution architecture, AI architecture, software architecture, or closely related technical roles.
- Proven experience designing and implementing enterprise AI and Generative AI solutions, including model integration patterns, enterprise AI platforms, and production architecture considerations.
- Strong understanding of modern AI solution approaches, including Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), vector-based search, enterprise search, prompt orchestration, agentic AI, and multi-agent workflows.
- Demonstrated experience conducting business discovery sessions, gathering business and functional requirements, and translating requirements into scalable solution architecture recommendations.
- Experience designing enterprise solutions across cloud, on-premises, or hybrid environments, using platforms such as Microsoft Azure, AWS, or Google Cloud Platform (GCP).
- Strong knowledge of APIs, microservices, cloud-native architectures, containerized workloads, distributed computing, and enterprise identity/security integration.
- Experience guiding internal development teams through solution design, implementation planning, architecture reviews, and technical decision-making.
- Ability to create reference implementations, lightweight prototypes, and technical demonstrations to validate or communicate proposed architecture approaches.
- Working knowledge of Python, JavaScript/TypeScript, REST APIs, SDKs, notebooks, and containerized application patterns sufficient to evaluate technical approaches and guide development teams.
- Experience documenting architecture designs, solution assumptions, dependencies, implementation considerations, and technical recommendations.
- Strong analytical thinking, technical problem-solving, communication, facilitation, and cross-functional collaboration skills.
- Ability to communicate complex technical concepts to both business stakeholders and engineering teams.
- Ability to work 100% onsite in Peoria, IL, or Irving, TX, five days per week, starting Day 1.
PREFERRED QUALIFICATIONS
- Experience with enterprise AI platforms and services such as Azure OpenAI, Amazon Bedrock, Google Vertex AI, Microsoft Foundry, or Accenture AI Refinery.
- Experience with AI development frameworks and orchestration technologies such as LangChain, LangGraph, GraphRAG, and agentic AI frameworks.
- Familiarity with Model Context Protocol (MCP) servers, agent skills, agent harness patterns, and multi-agent orchestration architectures.
- Experience with enterprise data and AI platforms such as Snowflake or Dataiku.
- Experience with Kubernetes, Azure Kubernetes Service (AKS), Amazon Elastic Kubernetes Service (EKS), or comparable enterprise container orchestration environments.
- Experience creating reusable enterprise AI architecture patterns, reference implementations, solution accelerators, or technical frameworks.
- Familiarity with enterprise AI governance, security considerations, model integration, and scalable production deployment practices.
- Experience evaluating AI solution feasibility, technical dependencies, architecture tradeoffs, and integration requirements.
- Cloud certifications in AWS, Microsoft Azure, or Google Cloud Platform.
- Previous experience supporting enterprise-scale AI transformation, industrial technology, manufacturing, or globally distributed engineering organizations.
WHY THIS ROLE?
This position offers an opportunity to shape enterprise AI and Generative AI solutions supporting a global leader in heavy machinery and manufacturing. You will collaborate with business leaders, enterprise architects, and technical teams to define innovative AI use cases, design scalable architectures, and guide the adoption of emerging technologies such as RAG, agentic AI, and cloud-based AI platforms. Cullerton Group provides a professional environment with growth potential, comprehensive benefits, and opportunities to develop specialized expertise in enterprise AI architecture and digital transformation.
INTRODUCTION
Cullerton Group has a new opportunity for a Solutions Architect 5 – Enterprise AI & Generative AI Architect in Peoria, Illinois, or Irving, Texas. The work will be performed 100% onsite, five days per week, with no remote or hybrid options. This is a 12-month position that can lead to permanent employment with our client. Compensation is $112/hr W-2 plus full benefits (vision, dental, health insurance, 401k, and holiday pay).
JOB SUMMARY
The Solutions Architect 5 will support the design and development of enterprise Artificial Intelligence (AI), Generative AI, and data-driven solutions across complex cloud and on-premises environments. This role focuses on collaborating with business stakeholders, enterprise architects, and technical development teams to identify business needs, define AI use cases, and translate requirements into scalable solution architectures. The architect will evaluate technologies, integration patterns, and architectural tradeoffs while providing technical guidance, reference implementations, and implementation recommendations. The ideal candidate will have extensive enterprise solution architecture experience, strong knowledge of modern AI technologies such as Retrieval-Augmented Generation (RAG) and agentic AI, and the ability to bridge business requirements with practical technical solutions.
KEY RESPONSIBILITIES
- Lead and participate in business discovery sessions, collaborating with stakeholders to identify objectives, pain points, functional requirements, constraints, and opportunities for enterprise AI and Generative AI solutions.
- Design and recommend scalable enterprise AI solution architectures, including RAG, vector search, enterprise search, prompt orchestration, agentic AI, multi-agent workflows, and model integration patterns.
- Evaluate cloud, on-premises, and hybrid architecture options, including APIs, microservices, containerized applications, distributed computing platforms, and enterprise identity/security integration.
- Provide architectural guidance to internal development teams, including implementation planning, solution feasibility analysis, technical tradeoff evaluations, reference implementations, and lightweight prototypes.
- Collaborate with enterprise architects, data scientists, machine learning engineers, application developers, and business stakeholders to create architecture documentation, reusable design patterns, technical recommendations, and implementation roadmaps.
BASIC QUALIFICATIONS
- Bachelor's degree in Computer Science, Information Technology, Software Engineering, Computer Engineering, or a closely related technical discipline.
- 10+ years of relevant professional experience in enterprise solution architecture, AI architecture, software architecture, or closely related technical roles.
- Proven experience designing and implementing enterprise AI and Generative AI solutions, including model integration patterns, enterprise AI platforms, and production architecture considerations.
- Strong understanding of modern AI solution approaches, including Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), vector-based search, enterprise search, prompt orchestration, agentic AI, and multi-agent workflows.
- Demonstrated experience conducting business discovery sessions, gathering business and functional requirements, and translating requirements into scalable solution architecture recommendations.
- Experience designing enterprise solutions across cloud, on-premises, or hybrid environments, using platforms such as Microsoft Azure, AWS, or Google Cloud Platform (GCP).
- Strong knowledge of APIs, microservices, cloud-native architectures, containerized workloads, distributed computing, and enterprise identity/security integration.
- Experience guiding internal development teams through solution design, implementation planning, architecture reviews, and technical decision-making.
- Ability to create reference implementations, lightweight prototypes, and technical demonstrations to validate or communicate proposed architecture approaches.
- Working knowledge of Python, JavaScript/TypeScript, REST APIs, SDKs, notebooks, and containerized application patterns sufficient to evaluate technical approaches and guide development teams.
- Experience documenting architecture designs, solution assumptions, dependencies, implementation considerations, and technical recommendations.
- Strong analytical thinking, technical problem-solving, communication, facilitation, and cross-functional collaboration skills.
- Ability to communicate complex technical concepts to both business stakeholders and engineering teams.
- Ability to work 100% onsite in Peoria, IL, or Irving, TX, five days per week, starting Day 1.
PREFERRED QUALIFICATIONS
- Experience with enterprise AI platforms and services such as Azure OpenAI, Amazon Bedrock, Google Vertex AI, Microsoft Foundry, or Accenture AI Refinery.
- Experience with AI development frameworks and orchestration technologies such as LangChain, LangGraph, GraphRAG, and agentic AI frameworks.
- Familiarity with Model Context Protocol (MCP) servers, agent skills, agent harness patterns, and multi-agent orchestration architectures.
- Experience with enterprise data and AI platforms such as Snowflake or Dataiku.
- Experience with Kubernetes, Azure Kubernetes Service (AKS), Amazon Elastic Kubernetes Service (EKS), or comparable enterprise container orchestration environments.
- Experience creating reusable enterprise AI architecture patterns, reference implementations, solution accelerators, or technical frameworks.
- Familiarity with enterprise AI governance, security considerations, model integration, and scalable production deployment practices.
- Experience evaluating AI solution feasibility, technical dependencies, architecture tradeoffs, and integration requirements.
- Cloud certifications in AWS, Microsoft Azure, or Google Cloud Platform.
- Previous experience supporting enterprise-scale AI transformation, industrial technology, manufacturing, or globally distributed engineering organizations.
WHY THIS ROLE?
This position offers an opportunity to shape enterprise AI and Generative AI solutions supporting a global leader in heavy machinery and manufacturing. You will collaborate with business leaders, enterprise architects, and technical teams to define innovative AI use cases, design scalable architectures, and guide the adoption of emerging technologies such as RAG, agentic AI, and cloud-based AI platforms. Cullerton Group provides a professional environment with growth potential, comprehensive benefits, and opportunities to develop specialized expertise in enterprise AI architecture and digital transformation.
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Senior Level Generative AI Analyst Jobs: Frequently Asked Questions
How do I get a senior level generative ai analyst job?
Employers at this level look for candidates who have owned the full lifecycle of generative AI projects, from problem framing through production deployment, not just contributed to them. A strong portfolio of measurable business outcomes, experience guiding junior analysts or cross-functional teams, and deep familiarity with large language model evaluation and prompt engineering give candidates a clear edge. Published work, patents, or conference presentations reinforce technical credibility.
Which companies hire senior level generative ai analysts?
Companies hiring senior level generative ai analysts right now include NVIDIA, Amazon, and Cullerton, based on current listings on Migrate Mate as of November 2026. Hiring at this level tends to come from large technology firms and consulting organizations building out dedicated AI practices, as well as enterprises in financial services, healthcare, and media actively scaling generative AI capabilities.
Are there remote senior level generative ai analyst jobs?
Yes, remote and hybrid availability is strong at this level. About 25% of senior level generative ai analyst openings are remote or hybrid as of November 2026, reflecting employer confidence that experienced professionals can operate independently and lead distributed teams. Fully on-site roles do still exist, particularly in regulated industries or organizations running early-stage AI programs.
What makes a generative ai analyst role senior level?
Senior level generative ai analyst roles are defined by ownership and scope rather than task execution. At this stage, you are expected to set the technical direction for generative AI initiatives, make architecture and tooling decisions, and take accountability for business outcomes. Mentoring junior analysts, influencing stakeholders outside the data team, and driving adoption across an organization are standard expectations, not optional contributions.
Which industries hire the most senior level generative ai analysts?
Senior Level generative ai analyst roles concentrate in Technology & Software, Retail, and Law & Legal Services, based on current listings on Migrate Mate as of November 2026. These sectors are driving demand because they have both the data infrastructure to support large-scale generative AI deployment and the business pressure to move from pilot programs to production-grade systems, creating a need for experienced professionals who can lead that transition.