AI Tech Lead Jobs
AI Tech Lead jobs are open across technology, finance, and healthcare, from senior engineer to principal and director level, with specializations in machine learning infrastructure, generative AI systems, and applied AI product development. Find a role that fits below and apply directly.
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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
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
AI Tech Lead Jobs by Experience Level
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Find AI Tech Lead JobsAI Tech Lead Job Market
Who's Hiring
- Booz Allen Hamilton20
- Boston Consulting18
- JPMorganChase13
- Johns Hopkins University Applied Physics Laboratory9
- University of San Diego6
Top Industries Hiring
- Technology & Software15
- Education8
- Banking & Financial Services5
- Healthcare & Medical Services5
- Insurance3
What Employers Look For
The qualifications that appear most often in AI tech lead jobs.
- 5+ years of experience building and deploying machine learning or AI systems in production
- Proficiency in Python and deep familiarity with frameworks such as PyTorch or TensorFlow
- Experience with cloud AI services on AWS, Azure, or Google Cloud Platform
- Demonstrated ability to lead cross-functional engineering teams on AI-driven products
- Strong understanding of MLOps practices including model monitoring, versioning, and CI/CD pipelines
- Bachelor's or master's degree in computer science, data science, or a related technical field
Tips for Your AI Tech Lead Job Search
Tailor your resume to model deployment
Hiring managers for ai tech lead roles want to see you've shipped production AI systems, not just experimented in notebooks. Highlight specific models you've deployed, the infrastructure you used, and the business outcomes your work drove.
Distinguish technical leadership from management
Many ai tech lead job descriptions blend hands-on engineering with team direction. Be explicit on your resume about which decisions you owned technically versus managerially, since some roles want an individual contributor lead and others want a people manager.
Apply early to roles that fit
Migrate Mate lists ai tech lead openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target companies with active AI initiatives
Job postings referencing MLOps, LLM fine-tuning, or responsible AI signal a team already past the proof-of-concept stage. Those roles move faster and give you more room to lead technical direction from day one than roles where AI is still exploratory.
Prepare a system design narrative for interviews
AI tech lead interviews often include an open-ended system design round focused on scalable model serving or data pipelines. Practice explaining not just what you built but why you made specific architectural trade-offs under real constraints like latency, cost, or data availability.
Negotiate scope alongside compensation
When you receive an offer, clarify exactly what technical authority the role carries: budget, vendor decisions, architecture sign-off, and hiring input. AI tech lead titles vary widely in actual scope, and getting clarity before you accept prevents misalignment on day one.
AI Tech Lead Jobs: Frequently Asked Questions
Which companies are hiring the most ai tech leads?
The most active employers for ai tech leads right now are Booz Allen Hamilton, Boston Consulting, and JPMorganChase, and the most openings are in California, Texas, and Virginia, based on current listings on Migrate Mate as of November 2026. Demand is concentrated in companies scaling applied AI products rather than pure research organizations.
How many ai tech lead jobs are remote?
About 54% of ai tech lead openings are fully remote or hybrid as of November 2026, reflecting strong employer flexibility for experienced AI engineers. Roles focused on model development and architecture review tend to be more remote-friendly, while positions requiring close collaboration with hardware or data infrastructure teams are more likely to be on-site.
How do you become an ai tech lead?
Start by building deep expertise in a core AI discipline such as machine learning engineering, NLP, or computer vision, then consistently take on work that crosses team boundaries: reviewing architecture decisions, mentoring junior engineers, and driving technical roadmap conversations. Lead at least one end-to-end production deployment where you own the full system design, then document the trade-offs you made in a portfolio or case study. Employers promote or hire ai tech leads based on evidence of technical judgment and cross-functional influence, not tenure alone.
Can I get an ai tech lead job with little or no prior lead experience?
Yes, but you'll need to demonstrate lead-level impact even if your title hasn't caught up yet. Contribute to open-source AI projects where you review others' code or set architectural direction, volunteer to run technical design reviews at your current job, and document instances where your recommendations changed how a team approached a problem. Applying to companies that are building out new AI teams is often more realistic than targeting organizations with established AI hierarchies, since those teams need people to define the technical culture from scratch.
What does the ai tech lead interview process look like?
Most ai tech lead interview processes include a recruiter screen, a technical phone interview covering machine learning fundamentals and system design, and a multi-stage onsite or virtual loop. The loop typically combines a hands-on coding or modeling exercise, an AI system design session where you architect a scalable solution end-to-end, a behavioral round focused on technical leadership and cross-functional influence, and a conversation with engineering or product leadership about roadmap priorities and team direction.
Where can I find and apply to ai tech lead jobs?
You can find and apply to ai tech lead jobs on Migrate Mate, which lists current openings from companies across the United States. Search the listings to find roles that match your experience level and specialization, then apply directly to each one that fits.
See All 349+ AI Tech Lead Jobs
Find roles that match your experience and apply in just a few clicks.
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