Director of AI Engineering Jobs
Director of AI Engineering jobs are open across technology, finance, healthcare, and enterprise software, at every level from senior staff engineer to VP of engineering, with specializations in machine learning infrastructure, generative AI systems, and applied research. Scan the live roles below and apply to whichever ones fit.
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Karman Space & Defense is a leader in the rapid design, development, and production of critical, next-generation system solutions that align with the U.S. Department of War and its allies' core mission priorities, and meet the accelerating demand for access to space. Building on nearly 50 years of success, we deliver Payload & Protection Systems, Aero/Hydrodynamic Interstage Systems, and Propulsion & Launch Systems to more than 80 prime contractors supporting over 130 space and defense programs.
In this senior individual‑contributor role, you will translate Karman's enterprise artificial intelligence (AI) strategy into secure, scalable, and reusable architectures that accelerate responsible adoption across the organization. You will shape technical patterns, build reference implementations, evaluate emerging technologies, and establish guardrails that enable governed self‑service. Partnering closely with AI/Data & Automation, Business Systems, Cybersecurity, Infrastructure, and functional teams, you will provide hands‑on technical leadership that strengthens solution quality, operational consistency, and enterprise readiness.
Responsibilities
- Defines and evolves Karman's enterprise AI reference architecture.
- Establishes architectural patterns for model access, retrieval‑augmented generation (RAG), orchestration, agents, application programming interfaces (APIs), evaluation, observability, and cost management.
- Develops technical standards for Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, and other approved enterprise AI capabilities.
- Evaluates external platforms and models based on security, performance, cost, and business needs.
- Designs architectures that support portability using APIs, abstraction layers, open protocols, and exit strategies.
- Builds proofs of concept, reference implementations, reusable components, and technical accelerators.
- Provides hands‑on support for complex AI use cases, including RAG solutions, agentic workflows, model‑integrated applications, and automation.
- Establishes reusable templates, patterns, guardrails, and documentation to enable governed self‑service.
- Translates enterprise AI governance requirements into technical controls for identity, access, logging, monitoring, classification, retention, and human oversight.
- Defines evaluation, observability, quality, operational readiness, incident response, and lifecycle management standards for AI solutions.
Required Qualifications
- Significant experience in enterprise architecture, cloud architecture, software engineering, or platform engineering.
- Recent experience designing and deploying production generative artificial intelligence (AI) solutions.
- Strong working knowledge of the Microsoft cloud and AI ecosystem, including Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, Entra ID, Purview, and Microsoft security technologies.
- Hands‑on experience integrating commercial AI models through APIs and building model‑enabled applications.
- Strong understanding of RAG architectures, agent patterns, tool use, model orchestration, vector search, and enterprise data integration.
- Familiarity with standards and technologies such as Model Context Protocol (MCP), OpenTelemetry, open‑weight models, and modern inference platforms.
- Experience designing secure systems in regulated or high‑assurance environments.
- Ability and willingness to write code, build prototypes, review designs, and troubleshoot technical issues.
- Strong written and verbal communication skills with the ability to explain complex technical tradeoffs to technical and executive audiences.
Preferred Qualifications
- Experience in aerospace, defense, government, manufacturing, financial services, or other regulated industries.
- Experience with GCC High, sovereign cloud, restricted environments, or controlled‑data architectures.
- Familiarity with frameworks such as Cybersecurity Maturity Model Certification (CMMC), National Institute of Standards and Technology (NIST) Special Publication 800‑171, Controlled Unclassified Information (CUI), International Traffic in Arms Regulations (ITAR), NIST AI Risk Management Framework (RMF), and International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 42001.
- Experience integrating AI with enterprise platforms such as enterprise resource planning (ERP), product lifecycle management (PLM), manufacturing execution systems (MES), customer relationship management (CRM), productivity, analytics, and workflow systems.
- Familiarity with graphics processing unit (GPU) infrastructure, self‑hosted models, fine‑tuning, and private inference environments.
- Relevant Microsoft, cloud, architecture, or security certifications.
This position requires U.S. person status under U.S. export control laws, including U.S. citizens and nationals, lawful permanent residents, refugees, and asylees.
Benefits
- Medical, dental, and vision insurance
- 401(k) with company match
- Paid time off
- Health Savings Account (HSA) with company contribution
- Flexible Spending Accounts (FSA)
- Company‑paid life and ADdummyamp;D insurance
- Short‑ and long‑term disability coverage
- Tuition reimbursement
Karman Space and Defense is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, protected veteran status, or any other status protected by applicable federal, state, or local law.
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Karman Space & Defense is a leader in the rapid design, development, and production of critical, next-generation system solutions that align with the U.S. Department of War and its allies' core mission priorities, and meet the accelerating demand for access to space. Building on nearly 50 years of success, we deliver Payload & Protection Systems, Aero/Hydrodynamic Interstage Systems, and Propulsion & Launch Systems to more than 80 prime contractors supporting over 130 space and defense programs.
In this senior individual‑contributor role, you will translate Karman's enterprise artificial intelligence (AI) strategy into secure, scalable, and reusable architectures that accelerate responsible adoption across the organization. You will shape technical patterns, build reference implementations, evaluate emerging technologies, and establish guardrails that enable governed self‑service. Partnering closely with AI/Data & Automation, Business Systems, Cybersecurity, Infrastructure, and functional teams, you will provide hands‑on technical leadership that strengthens solution quality, operational consistency, and enterprise readiness.
Responsibilities
- Defines and evolves Karman's enterprise AI reference architecture.
- Establishes architectural patterns for model access, retrieval‑augmented generation (RAG), orchestration, agents, application programming interfaces (APIs), evaluation, observability, and cost management.
- Develops technical standards for Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, and other approved enterprise AI capabilities.
- Evaluates external platforms and models based on security, performance, cost, and business needs.
- Designs architectures that support portability using APIs, abstraction layers, open protocols, and exit strategies.
- Builds proofs of concept, reference implementations, reusable components, and technical accelerators.
- Provides hands‑on support for complex AI use cases, including RAG solutions, agentic workflows, model‑integrated applications, and automation.
- Establishes reusable templates, patterns, guardrails, and documentation to enable governed self‑service.
- Translates enterprise AI governance requirements into technical controls for identity, access, logging, monitoring, classification, retention, and human oversight.
- Defines evaluation, observability, quality, operational readiness, incident response, and lifecycle management standards for AI solutions.
Required Qualifications
- Significant experience in enterprise architecture, cloud architecture, software engineering, or platform engineering.
- Recent experience designing and deploying production generative artificial intelligence (AI) solutions.
- Strong working knowledge of the Microsoft cloud and AI ecosystem, including Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, Entra ID, Purview, and Microsoft security technologies.
- Hands‑on experience integrating commercial AI models through APIs and building model‑enabled applications.
- Strong understanding of RAG architectures, agent patterns, tool use, model orchestration, vector search, and enterprise data integration.
- Familiarity with standards and technologies such as Model Context Protocol (MCP), OpenTelemetry, open‑weight models, and modern inference platforms.
- Experience designing secure systems in regulated or high‑assurance environments.
- Ability and willingness to write code, build prototypes, review designs, and troubleshoot technical issues.
- Strong written and verbal communication skills with the ability to explain complex technical tradeoffs to technical and executive audiences.
Preferred Qualifications
- Experience in aerospace, defense, government, manufacturing, financial services, or other regulated industries.
- Experience with GCC High, sovereign cloud, restricted environments, or controlled‑data architectures.
- Familiarity with frameworks such as Cybersecurity Maturity Model Certification (CMMC), National Institute of Standards and Technology (NIST) Special Publication 800‑171, Controlled Unclassified Information (CUI), International Traffic in Arms Regulations (ITAR), NIST AI Risk Management Framework (RMF), and International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 42001.
- Experience integrating AI with enterprise platforms such as enterprise resource planning (ERP), product lifecycle management (PLM), manufacturing execution systems (MES), customer relationship management (CRM), productivity, analytics, and workflow systems.
- Familiarity with graphics processing unit (GPU) infrastructure, self‑hosted models, fine‑tuning, and private inference environments.
- Relevant Microsoft, cloud, architecture, or security certifications.
This position requires U.S. person status under U.S. export control laws, including U.S. citizens and nationals, lawful permanent residents, refugees, and asylees.
Benefits
- Medical, dental, and vision insurance
- 401(k) with company match
- Paid time off
- Health Savings Account (HSA) with company contribution
- Flexible Spending Accounts (FSA)
- Company‑paid life and ADdummyamp;D insurance
- Short‑ and long‑term disability coverage
- Tuition reimbursement
Karman Space and Defense is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, protected veteran status, or any other status protected by applicable federal, state, or local law.
View our Privacy Policy
Director of AI Engineering Jobs by Experience Level
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Find JobsDirector of AI Engineering Job Market
Who's Hiring
- JPMorganChase18
- Booz Allen Hamilton11
- Johns Hopkins University Applied Physics Laboratory9
- University of San Diego6
- Penn State University4
Top Industries Hiring
- Education
- Technology & Software
- Banking & Financial Services
- Healthcare & Medical Services
- Investment & Asset Management
What Employers Look For
The qualifications that appear most often in director of AI engineering jobs.
- 7 or more years of machine learning or AI engineering experience, including people management
- Proven ability to lead and scale AI or ML engineering teams of five or more engineers
- Deep experience with large language models, generative AI, or production ML systems
- Proficiency in Python and familiarity with MLOps tooling such as Kubernetes, MLflow, or Ray
- Experience deploying and optimizing models on cloud platforms such as AWS, Azure, or GCP
- Bachelor's or master's degree in computer science, machine learning, or a related technical field
Tips for Your Director of AI Engineering Job Search
Quantify model impact on your resume
Hiring managers for director of ai engineering roles want to see outcomes, not just model names. Replace 'built an LLM pipeline' with the latency reduction, cost savings, or accuracy improvement that resulted. Numbers turn a list of technologies into a leadership narrative.
Highlight cross-functional ownership experience
Director of ai engineering roles almost always require managing relationships with product, data science, and business stakeholders. If your resume only reflects engineering output, add a line showing you drove alignment across those groups on a shipped AI product.
Filter openings by infrastructure stack fit
AI engineering director roles split hard between cloud-native shops using managed ML platforms and teams building custom inference infrastructure. Reading the job description closely for which model serving approach they use saves you from tailoring an application to a stack you don't want.
Apply early to roles that fit
Migrate Mate lists director of ai engineering openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare a system design story for the interview
Most director of ai engineering interviews include a deep-dive where you walk through an AI system you designed end to end. Prepare one story that covers data pipeline, model selection, evaluation, deployment, and how you handled a failure. Vague answers at this stage are disqualifying.
Negotiate scope before discussing compensation
At the director level, what you own matters as much as pay. Before the offer conversation moves to numbers, clarify whether the role controls its own compute budget, owns model deployment, and has headcount authority. Scope misalignment shows up fast after you start.
Director of AI Engineering Jobs: Frequently Asked Questions
Which companies are hiring the most director of ai engineerings?
JPMorganChase, Booz Allen Hamilton, and Johns Hopkins University Applied Physics Laboratory are hiring the most director of ai engineerings right now, with openings concentrated in California, Texas, and New York, based on current listings on Migrate Mate as of November 2026. Roles at these companies tend to focus on scaling production AI systems and building out applied research functions.
How many director of ai engineering jobs are remote?
About 59% of director of ai engineering openings are fully remote or hybrid as of November 2026, with remote availability concentrated in roles focused on platform engineering, MLOps, and generative AI product development rather than hands-on hardware or on-premise infrastructure work.
How do you become a director of ai engineering?
Most directors of ai engineering start as individual contributors in machine learning or software engineering, move into a staff or principal engineer role where they lead technical direction, and then take on a team lead or engineering manager position. Building a record of shipping AI systems end to end, mentoring engineers, and influencing product roadmaps is what creates the profile that gets considered for director-level openings.
Can you get a director of ai engineering job without prior director experience?
Yes, candidates who have held a staff engineer or engineering manager title and led an AI team through a full product cycle are regularly considered. The gap to close is demonstrating organizational influence: owning a cross-functional initiative, setting technical strategy for a team, and showing you've managed hiring or performance. Those experiences matter more than the title on a previous role.
What does the director of ai engineering interview process look like?
The process typically runs four to six rounds and includes a recruiter screen, a technical depth interview covering model architecture and system design, a leadership interview focused on how you've built or grown an AI team, and a cross-functional round with product or business stakeholders. Some companies add a written case study or a presentation where you propose an AI strategy for a hypothetical product problem.
Where can I find and apply to director of ai engineering jobs?
You can find and apply to director of ai engineering jobs on Migrate Mate, which lists current openings from across the United States. Search the roles available, find the ones that match your experience and the kind of AI work you want to do, and apply directly to each listing that fits.
See All 305+ Director of AI Engineering Jobs
Find roles that match your experience and apply in just a few clicks.
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