Director of Solutions Engineering Jobs
Director of Solutions Engineering jobs are open across enterprise software, cloud infrastructure, cybersecurity, and fintech, at levels from senior manager to VP, with specializations in pre-sales architecture, technical consulting, and customer success engineering. See the openings below and apply to the ones that match your experience.
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Showing 5 of 30+ Director of Solutions Engineering jobs
Requisition ID: 279038
Salary Range: -
Please note that the Salary Range shown is a guideline only. Salary offered may vary based on factors, including, but not limited to, the successful candidate’s relevant knowledge, skills, and experience.
Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.
Purpose
The Director, AI Solutions Engineering, is a senior engineering leader responsible for translating priority business opportunities into secure, scalable, production-grade AI solutions. Working in partnership with business lines, product teams, data teams, risk partners, and the enterprise AI platform organization, the Director leads a portfolio of AI use cases from discovery and prioritization through implementation, adoption, and value realization.
The role leads cross-functional solution pods, establishes reusable delivery patterns, and ensures predictive AI, generative AI, and agentic solutions are measurable, responsible, reliable, and aligned with enterprise architecture and governance standards. The Director also develops engineering talent and provides hands-on technical leadership across software, data, platform integration, MLOps, and LLMOps.
This role reports to the Senior Director level, with accountability for an assigned solution portfolio, delivery teams, and business-line partnerships rather than the overall function-wide strategy.
What You'll Do
- Develop and manage the roadmap for an assigned portfolio of AI solutions and priority business use cases, aligned with enterprise AI strategy, intake, funding, governance, and platform direction.
- Partner with business and technology leaders to identify, assess, and prioritize high-value opportunities such as GenAI assistants, intelligent document processing, agentic workflows, decision support, personalization, and risk-signal generation.
- Translate business objectives into practical delivery plans with clear scope, data dependencies, architectural decisions, control requirements, adoption measures, and expected business outcomes.
- Maintain solution blueprints and reusable implementation patterns that allow other business-line teams to replicate proven approaches.
- Lead cross-functional delivery pods comprising AI and software engineers, product owners, business SMEs, data specialists, platform teams, cybersecurity, architecture, risk, privacy, and compliance partners.
- Own delivery outcomes for assigned solutions, including technical quality, schedule, risk management, production readiness, adoption, operational performance, and measurable value.
- Provide hands-on engineering enablement through reference implementations, architecture guidance, reusable CI/CD modules, evaluation harnesses, coding standards, and design reviews.
- Create transparent delivery governance using milestones, dependency tracking, risk registers, service-level objectives, and executive-ready status reporting.
- Oversee design and implementation of predictive AI, GenAI, retrieval-augmented generation, and agentic solutions across cloud-native application and data platforms.
- Ensure MLOps and LLMOps practices cover evaluation, versioning, deployment, monitoring, rollback, drift detection, prompt management, model and tool changes, and production incident response.
- Maintain reusable patterns for APIs, microservices, prompts, embeddings, vector stores, tool integration, runtime orchestration, observability, resilience, and cost management.
- Integrate AI engineering into the software-development lifecycle and developer toolchain, including source repositories, automated testing, CI/CD, security scanning, and engineering productivity tooling.
- Guide solution teams in building secure batch and real-time data-processing pipelines that ingest, validate, transform, reconcile, enrich, and distribute structured and unstructured data.
- Ensure AI solutions apply appropriate data-quality, lineage, metadata, retention, access-control, privacy, and observability standards.
- Provide direction for solutions that use Customer Data Platforms and customer-360 capabilities, including identity resolution, unified customer profiles, segmentation, consent and preference management, personalization, and downstream activation.
- Partner with enterprise data and customer-data teams to define integration patterns between operational systems, governed data products, Customer Data Platforms, analytics environments, and AI applications.
- Embed privacy, data classification, model governance, explainability, evaluation, human oversight, safety, security, and monitoring controls into solution design and delivery.
- Ensure solutions follow enterprise AI intake, architecture, cybersecurity, model-risk, third-party risk, privacy, and production-governance processes.
- Operationalize risk and control requirements through delivery checklists, automated evidence, approval gates, testing standards, and ongoing monitoring rather than documentation-only compliance.
- Promote responsible experimentation while maintaining clear boundaries for sensitive data, regulated decisions, external models, tools, agents, and production access.
- Partner with AI Platform Engineering and enterprise architecture to translate solution demand into platform requirements and improve shared capabilities such as evaluation, guardrails, model routing, feature and embedding stores, vector search, and agent frameworks.
- Drive adoption of enterprise platform services, standard APIs, reusable components, and golden paths to reduce duplication, delivery time, operational risk, and run costs.
- Define and track KPIs and OKRs for assigned solutions, including cycle time, adoption, quality, reliability, risk posture, productivity, customer or employee outcomes, and return on investment.
- Provide concise portfolio reporting and decision recommendations to business sponsors, CIO organizations, data and AI leadership, risk partners, and executive governance forums.
- Lead, coach, and develop a high-performing team of AI solutions engineers and technical leads, setting clear expectations for delivery, engineering excellence, responsible AI, and business partnership.
- Build workforce and capability plans for the assigned portfolio, including hiring, succession planning, skill development, performance enablement, and deployment of specialists across solution pods.
- Foster an inclusive, accountable engineering culture that encourages constructive challenge, continuous learning, reuse, operational ownership, and evidence-based decision-making.
- Contribute to the broader AI engineering community of practice through technical standards, reusable assets, mentoring, and knowledge sharing.
What You'll Bring
- 13+ years of experience in software engineering, data engineering, AI/ML engineering, solution architecture, or technology delivery, including at least 5 years leading engineering teams or complex cross-functional delivery.
- Demonstrated success delivering production AI/ML, GenAI, or data-intensive solutions at enterprise scale, preferably in financial services or another regulated industry.
- Proven leadership of cross-functional pods delivering measurable business outcomes in partnership with business-line product owners, domain experts, data teams, and technology stakeholders.
- Strong knowledge of GenAI and agentic-system patterns, retrieval and embeddings, vector databases, prompt and policy engineering, model evaluation, guardrails, and MLOps/LLMOps.
- Strong command of cloud-native engineering on Google Cloud Platform, Microsoft Azure, or comparable platforms, including APIs, microservices, event-driven systems, containers, CI/CD, observability, and reliability engineering.
- Working knowledge of modern data processing using technologies such as SQL, Python, Apache Spark, Databricks, Kafka, Dataflow, or equivalent batch and streaming services.
- Working knowledge of Customer Data Platform and customer-360 concepts, including profile unification, identity resolution, segmentation, consent, personalization, activation, customer data quality, and lineage.
- Experience establishing reusable solution patterns, reference architectures, engineering standards, and production-readiness practices that improve delivery speed and reliability across multiple teams.
- Demonstrated ability to implement responsible AI, cybersecurity, privacy, data-governance, and model-risk requirements within engineering delivery.
- Strong stakeholder leadership with the ability to influence senior business, technology, data, risk, and compliance partners and align teams around shared outcomes.
- Experience defining and communicating solution value through KPIs, OKRs, adoption measures, reliability indicators, risk metrics, and financial or operational benefits.
- Proven ability to attract, coach, develop, and retain engineering talent while building an inclusive and high-accountability team culture.
- Bachelor’s or master’s degree in computer science, software engineering, data science, information systems, or a related field, or equivalent practical experience.
- Experience delivering AI or customer-data solutions within banking, insurance, capital markets, healthcare, telecommunications, or another highly regulated environment.
- Familiarity with Google Cloud Vertex AI, Azure Databricks, Apigee X, Azure API Management, LangGraph, Google ADK, MLflow, Kubernetes, and enterprise vector-search technologies.
- Exposure to Customer Data Platforms such as Adobe Experience Platform, Salesforce Data Cloud, Tealium, Twilio Segment, Microsoft Dynamics 365 Customer Insights, Treasure Data, or equivalent platforms.
- Knowledge of applicable Canadian regulatory and privacy expectations, including OSFI B-13, OSFI E-23, OSFI B-10, PIPEDA, and Quebec Law 25.
- Relevant cloud, architecture, data, security, or AI certifications from Google Cloud, Microsoft Azure, Databricks, or comparable providers.
- Enterprise thinking: connects business priorities to reusable solution patterns, data capabilities, platform services, and enterprise standards.
- Engineering excellence: establishes practical golden paths that improve quality, security, reliability, and time-to-production.
- Risk stewardship: embeds fit-for-purpose controls into engineering workflows without creating unnecessary delivery friction.
- Talent leadership: develops strong technical leaders and creates an environment of accountability, inclusion, and continuous improvement.
- Results orientation: ships production solutions, measures adoption and value, and uses operational evidence to continuously improve.
Interested?
If your experience is closely related but doesn’t align perfectly with every qualification, we do encourage you to apply - you might be the right candidate for this or other roles at Scotiabank!
At Scotiabank, every employee is empowered to reach their fullest potential, respected for who they are and, embraced for their differences. That’s why we work to grow and diversify talent and engage employees in a performance-oriented culture.
What's in it for you?
Scotiabank wants you to be able to bring your best self to work – and life, every day. With a focus on holistic well-being, our many flexible benefit programs are designed to help support your unique family, financial, physical, mental, and social health needs.
#Dallas
Location(s): United States : Texas : Dallas
Scotiabank is a leading bank in the Americas. Guided by our purpose: "for every future", we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.
At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.
Scotiabank is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by federal, state, or local law.
Requisition ID: 279038
Salary Range: -
Please note that the Salary Range shown is a guideline only. Salary offered may vary based on factors, including, but not limited to, the successful candidate’s relevant knowledge, skills, and experience.
Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.
Purpose
The Director, AI Solutions Engineering, is a senior engineering leader responsible for translating priority business opportunities into secure, scalable, production-grade AI solutions. Working in partnership with business lines, product teams, data teams, risk partners, and the enterprise AI platform organization, the Director leads a portfolio of AI use cases from discovery and prioritization through implementation, adoption, and value realization.
The role leads cross-functional solution pods, establishes reusable delivery patterns, and ensures predictive AI, generative AI, and agentic solutions are measurable, responsible, reliable, and aligned with enterprise architecture and governance standards. The Director also develops engineering talent and provides hands-on technical leadership across software, data, platform integration, MLOps, and LLMOps.
This role reports to the Senior Director level, with accountability for an assigned solution portfolio, delivery teams, and business-line partnerships rather than the overall function-wide strategy.
What You'll Do
- Develop and manage the roadmap for an assigned portfolio of AI solutions and priority business use cases, aligned with enterprise AI strategy, intake, funding, governance, and platform direction.
- Partner with business and technology leaders to identify, assess, and prioritize high-value opportunities such as GenAI assistants, intelligent document processing, agentic workflows, decision support, personalization, and risk-signal generation.
- Translate business objectives into practical delivery plans with clear scope, data dependencies, architectural decisions, control requirements, adoption measures, and expected business outcomes.
- Maintain solution blueprints and reusable implementation patterns that allow other business-line teams to replicate proven approaches.
- Lead cross-functional delivery pods comprising AI and software engineers, product owners, business SMEs, data specialists, platform teams, cybersecurity, architecture, risk, privacy, and compliance partners.
- Own delivery outcomes for assigned solutions, including technical quality, schedule, risk management, production readiness, adoption, operational performance, and measurable value.
- Provide hands-on engineering enablement through reference implementations, architecture guidance, reusable CI/CD modules, evaluation harnesses, coding standards, and design reviews.
- Create transparent delivery governance using milestones, dependency tracking, risk registers, service-level objectives, and executive-ready status reporting.
- Oversee design and implementation of predictive AI, GenAI, retrieval-augmented generation, and agentic solutions across cloud-native application and data platforms.
- Ensure MLOps and LLMOps practices cover evaluation, versioning, deployment, monitoring, rollback, drift detection, prompt management, model and tool changes, and production incident response.
- Maintain reusable patterns for APIs, microservices, prompts, embeddings, vector stores, tool integration, runtime orchestration, observability, resilience, and cost management.
- Integrate AI engineering into the software-development lifecycle and developer toolchain, including source repositories, automated testing, CI/CD, security scanning, and engineering productivity tooling.
- Guide solution teams in building secure batch and real-time data-processing pipelines that ingest, validate, transform, reconcile, enrich, and distribute structured and unstructured data.
- Ensure AI solutions apply appropriate data-quality, lineage, metadata, retention, access-control, privacy, and observability standards.
- Provide direction for solutions that use Customer Data Platforms and customer-360 capabilities, including identity resolution, unified customer profiles, segmentation, consent and preference management, personalization, and downstream activation.
- Partner with enterprise data and customer-data teams to define integration patterns between operational systems, governed data products, Customer Data Platforms, analytics environments, and AI applications.
- Embed privacy, data classification, model governance, explainability, evaluation, human oversight, safety, security, and monitoring controls into solution design and delivery.
- Ensure solutions follow enterprise AI intake, architecture, cybersecurity, model-risk, third-party risk, privacy, and production-governance processes.
- Operationalize risk and control requirements through delivery checklists, automated evidence, approval gates, testing standards, and ongoing monitoring rather than documentation-only compliance.
- Promote responsible experimentation while maintaining clear boundaries for sensitive data, regulated decisions, external models, tools, agents, and production access.
- Partner with AI Platform Engineering and enterprise architecture to translate solution demand into platform requirements and improve shared capabilities such as evaluation, guardrails, model routing, feature and embedding stores, vector search, and agent frameworks.
- Drive adoption of enterprise platform services, standard APIs, reusable components, and golden paths to reduce duplication, delivery time, operational risk, and run costs.
- Define and track KPIs and OKRs for assigned solutions, including cycle time, adoption, quality, reliability, risk posture, productivity, customer or employee outcomes, and return on investment.
- Provide concise portfolio reporting and decision recommendations to business sponsors, CIO organizations, data and AI leadership, risk partners, and executive governance forums.
- Lead, coach, and develop a high-performing team of AI solutions engineers and technical leads, setting clear expectations for delivery, engineering excellence, responsible AI, and business partnership.
- Build workforce and capability plans for the assigned portfolio, including hiring, succession planning, skill development, performance enablement, and deployment of specialists across solution pods.
- Foster an inclusive, accountable engineering culture that encourages constructive challenge, continuous learning, reuse, operational ownership, and evidence-based decision-making.
- Contribute to the broader AI engineering community of practice through technical standards, reusable assets, mentoring, and knowledge sharing.
What You'll Bring
- 13+ years of experience in software engineering, data engineering, AI/ML engineering, solution architecture, or technology delivery, including at least 5 years leading engineering teams or complex cross-functional delivery.
- Demonstrated success delivering production AI/ML, GenAI, or data-intensive solutions at enterprise scale, preferably in financial services or another regulated industry.
- Proven leadership of cross-functional pods delivering measurable business outcomes in partnership with business-line product owners, domain experts, data teams, and technology stakeholders.
- Strong knowledge of GenAI and agentic-system patterns, retrieval and embeddings, vector databases, prompt and policy engineering, model evaluation, guardrails, and MLOps/LLMOps.
- Strong command of cloud-native engineering on Google Cloud Platform, Microsoft Azure, or comparable platforms, including APIs, microservices, event-driven systems, containers, CI/CD, observability, and reliability engineering.
- Working knowledge of modern data processing using technologies such as SQL, Python, Apache Spark, Databricks, Kafka, Dataflow, or equivalent batch and streaming services.
- Working knowledge of Customer Data Platform and customer-360 concepts, including profile unification, identity resolution, segmentation, consent, personalization, activation, customer data quality, and lineage.
- Experience establishing reusable solution patterns, reference architectures, engineering standards, and production-readiness practices that improve delivery speed and reliability across multiple teams.
- Demonstrated ability to implement responsible AI, cybersecurity, privacy, data-governance, and model-risk requirements within engineering delivery.
- Strong stakeholder leadership with the ability to influence senior business, technology, data, risk, and compliance partners and align teams around shared outcomes.
- Experience defining and communicating solution value through KPIs, OKRs, adoption measures, reliability indicators, risk metrics, and financial or operational benefits.
- Proven ability to attract, coach, develop, and retain engineering talent while building an inclusive and high-accountability team culture.
- Bachelor’s or master’s degree in computer science, software engineering, data science, information systems, or a related field, or equivalent practical experience.
- Experience delivering AI or customer-data solutions within banking, insurance, capital markets, healthcare, telecommunications, or another highly regulated environment.
- Familiarity with Google Cloud Vertex AI, Azure Databricks, Apigee X, Azure API Management, LangGraph, Google ADK, MLflow, Kubernetes, and enterprise vector-search technologies.
- Exposure to Customer Data Platforms such as Adobe Experience Platform, Salesforce Data Cloud, Tealium, Twilio Segment, Microsoft Dynamics 365 Customer Insights, Treasure Data, or equivalent platforms.
- Knowledge of applicable Canadian regulatory and privacy expectations, including OSFI B-13, OSFI E-23, OSFI B-10, PIPEDA, and Quebec Law 25.
- Relevant cloud, architecture, data, security, or AI certifications from Google Cloud, Microsoft Azure, Databricks, or comparable providers.
- Enterprise thinking: connects business priorities to reusable solution patterns, data capabilities, platform services, and enterprise standards.
- Engineering excellence: establishes practical golden paths that improve quality, security, reliability, and time-to-production.
- Risk stewardship: embeds fit-for-purpose controls into engineering workflows without creating unnecessary delivery friction.
- Talent leadership: develops strong technical leaders and creates an environment of accountability, inclusion, and continuous improvement.
- Results orientation: ships production solutions, measures adoption and value, and uses operational evidence to continuously improve.
Interested?
If your experience is closely related but doesn’t align perfectly with every qualification, we do encourage you to apply - you might be the right candidate for this or other roles at Scotiabank!
At Scotiabank, every employee is empowered to reach their fullest potential, respected for who they are and, embraced for their differences. That’s why we work to grow and diversify talent and engage employees in a performance-oriented culture.
What's in it for you?
Scotiabank wants you to be able to bring your best self to work – and life, every day. With a focus on holistic well-being, our many flexible benefit programs are designed to help support your unique family, financial, physical, mental, and social health needs.
#Dallas
Location(s): United States : Texas : Dallas
Scotiabank is a leading bank in the Americas. Guided by our purpose: "for every future", we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.
At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.
Scotiabank is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by federal, state, or local law.
See All 30 Director of Solutions Engineering Jobs
Find roles that match your experience and apply in just a few clicks.
Find JobsDirector of Solutions Engineering Job Market
Who's Hiring
Top Industries Hiring
- Staffing & Recruiting
What Employers Look For
The qualifications that appear most often in director of solutions engineering jobs.
- 7 or more years of solutions engineering or sales engineering experience, including people management
- Demonstrated ability to lead and develop a team of solutions engineers or pre-sales architects
- Deep technical fluency in SaaS platforms, cloud infrastructure, or enterprise software
- Experience supporting enterprise sales cycles with proof-of-concept design and executive-level presentations
- Strong cross-functional collaboration with product, sales, and customer success leadership
- Bachelor's degree in computer science, engineering, or a related technical field
Tips for Your Director of Solutions Engineering Job Search
Quantify your pre-sales pipeline impact
Hiring managers for this role want to see revenue influenced, not just deals supported. List the aggregate contract value of deals your team technically closed, the win rate improvement you drove, and the headcount you managed to get there.
Align your resume to the buyer's journey
Directors of solutions engineering sit at the intersection of sales and product. Show how you've mapped technical demos and proof-of-concept engagements to specific deal stages, not just that you ran demos or managed a team of SEs.
Apply early to roles that fit
Migrate Mate lists director of solutions engineering openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target companies by sales motion type
Your experience in product-led growth, enterprise direct sales, or channel-partner models matters enormously here. Filter your search by company type so your background in one motion doesn't disqualify you in another where the SE team operates differently.
Prepare a team-building story for interviews
Expect at least one deep-dive question on how you've hired, ramped, and retained solutions engineers. Have a specific story ready about a hire who struggled and how you coached them, not just your overall hiring philosophy.
Negotiate scope alongside compensation
At this level, the size of territory, number of direct reports, and reporting line to CRO or CTO shape the role's leverage more than base pay alone. Clarify organizational structure before accepting so you're not stepping into a diminished mandate.
Director of Solutions Engineering Jobs: Frequently Asked Questions
Which companies are hiring the most director of solutions engineerings?
Intercom, Ahead, and AbbVie are hiring the most director of solutions engineerings right now, with openings concentrated in California, Texas, and Colorado, based on current listings on Migrate Mate as of November 2026. Enterprise software and cloud infrastructure companies consistently account for the largest share of open roles at this level.
How many director of solutions engineering jobs are remote?
About 86% of director of solutions engineering openings are fully remote or hybrid as of November 2026, reflecting the role's heavy reliance on video-based technical demos and virtual executive briefings. Pre-sales and cloud-focused sub-specializations tend to have the highest remote availability, while channel and partner engineering roles more often require regional travel.
How do you become a director of solutions engineering?
Start as an individual-contributor solutions engineer or pre-sales architect and build a record of closing complex enterprise deals through technical validation. Move into a team-lead or manager of solutions engineering role to gain people management experience, then position yourself for a director role by owning a regional territory, contributing to hiring, and presenting at the VP or C-suite level.
Can you get hired as a director of solutions engineering with limited leadership experience?
It's possible if you've informally led a pod of SEs, owned a strategic account segment, or built out a practice area from scratch, since formal title history matters less than demonstrated scope. Emphasize any work where you influenced team process, onboarded new hires, or collaborated directly with sales leadership to design the technical go-to-market motion for a product line.
What does the director of solutions engineering interview process look like?
Expect a recruiter screen followed by a hiring manager conversation focused on leadership philosophy and revenue impact, then a panel with sales, product, and customer success stakeholders assessing cross-functional fit. Most processes include a technical presentation or mock executive briefing, and a final conversation with a VP of sales or CRO to evaluate strategic alignment with the company's go-to-market approach.
Where can I find and apply to director of solutions engineering jobs?
You can find and apply to director of solutions engineering jobs on Migrate Mate, which lists current openings from across the United States in one place. Search for roles that match your industry background and seniority level, then apply directly to each listing that fits your experience.
See All 30 Director of Solutions Engineering Jobs
Find roles that match your experience and apply in just a few clicks.
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