AI Engineer Jobs
AI Engineer jobs are open across technology, healthcare, finance, and media, from new-grad to staff and principal levels, with common specializations in large language models, computer vision, and MLOps. Find a role that fits from the openings below and apply directly.
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Headquartered in California, East West Bank (Nasdaq: EWBC) is a top-performing commercial bank with a strong foundation, an enterprising spirit and a commitment to absolute integrity. East West Bank gives people the confidence to reach further.
The role is expected to be hands-on at the outset while helping establish foundational AI engineering capabilities, operating standards, and a small, high-performing AI engineering team.
- Design, develop, and deploy enterprise AI and Generative AI applications for prioritized banking use cases (e.g., customer service, fraud detection, document processing, knowledge management, and operational efficiency)
- Architect LLM-enabled solutions spanning retrieval-augmented generation, vector search, agentic workflows, MCP, model orchestration, tool/function calling, and human-in-the-loop controls.
- Build production-grade services and APIs using Python, FastAPI or Flask, Azure OpenAI, Azure ML, Databricks, ADLS, and modern cloud-native patterns.
- Integrate AI capabilities into enterprise applications, developer workflows, knowledge management platforms, automation, analytics, and decision-support processes.
- Establish engineering practices for CI/CD, testing, model evaluation, observability, performance optimization, security, and responsible AI controls.
- Establish reusable AI engineering frameworks, reference architectures, code standards, deployment patterns, and governance controls to accelerate enterprise adoption.
- Partner with business, data, cybersecurity, risk, compliance, legal, and vendor teams to ensure solutions meet regulatory, privacy, auditability, and operational risk expectations.
- Prototype rapidly with stakeholders, convert pilots into scalable implementations, and define measurable adoption and impact metrics.
- Evaluate LLM platforms for accuracy, latency, cost, security, explainability, and fit for regulated enterprise use cases.
- Support hiring, mentoring, and day-to-day technical leadership of AI engineers and cross-functional delivery teams.
- Stay current with emerging AI technologies and advise leadership on practical opportunities, risks, and implementation tradeoffs.
- Perform other duties as assigned.
AI Fluency & Hands-On LLM Skills
- Hands-on experience with major LLM platforms, including OpenAI ChatGPT/Codex, Anthropic Claude, Google Gemini, Microsoft Copilot/Azure OpenAI, AWS Bedrock, and open-source models such as Llama or Mistral.
- Practical experience with prompt engineering, RAG, embeddings, vector databases, LLM orchestration frameworks, agentic workflows, evaluation frameworks, and hallucination mitigation.
- Ability to design AI applications that include data protection, source validation, access control, logging, monitoring, traceability, and human review where appropriate.
- Strong understanding of Responsible AI, model governance, prompt-injection risks, data privacy, and production controls for LLM-enabled solutions.
- Bachelor's degree in Computer Science, Engineering, Data Science, AI/ML, or equivalent practical experience; advanced degree preferred.
- 10+ years of progressive experience in software engineering, AI engineering, platform engineering or related technology leadership roles, including experience delivering production AI solutions
- Proven experience leading AI, data, automation, or emerging technology initiatives from strategy and experimentation through production delivery.
- Strong hands-on engineering background in Python, API design, microservices, cloud architecture, distributed systems, data pipelines, CI/CD, testing, observability, and secure software delivery.
- Deep experience with the Azure ecosystem, including Azure OpenAI, Azure ML, Databricks, ADLS, Azure AI Search, and related enterprise integration patterns.
- Experience with LLM frameworks and tooling such as LangChain, LlamaIndex, Semantic Kernel, vector databases, model registries, evaluation frameworks, and monitoring/observability tools.
- Strong process and data discipline, including data quality, lineage, metadata, workflow design, controls, operational risk, and measurable business outcomes.
- Experience in financial services, banking, fintech, insurance, or another regulated industry with strong understanding of compliance, auditability, risk management, and governance.
- Ability to lead cross-functional teams, influence senior stakeholders, mentor engineers, and translate complex AI capabilities into practical business solutions.
- Strong executive communication skills, including the ability to define AI roadmaps, operating models, standards, adoption plans, and success metrics.
Preferred Qualifications
- Master's degree in AI, Computer Science, Data Science, Engineering, or a related field.
- Experience establishing AI engineering teams, platforms, reusable delivery patterns, and enterprise AI standards.
- Experience with copilots, enterprise search, intelligent document processing, workflow automation, and AI-enabled knowledge management.
- Experience driving AI vendor evaluation and selection processes within regulated environments
- Familiarity with model risk management, third-party/vendor risk, privacy impact assessments, and regulated technology delivery.
- Experience with MLOps/LLMOps, AI monitoring, evaluation pipelines, model/prompt registries, and production incident management.
- Track record of mentoring senior engineers and building high-performing technical teams.
Applicants must have legal authorization to work in the United States. We do not offer visa sponsorship at this time.
Headquartered in California, East West Bank (Nasdaq: EWBC) is a top-performing commercial bank with a strong foundation, an enterprising spirit and a commitment to absolute integrity. East West Bank gives people the confidence to reach further.
The role is expected to be hands-on at the outset while helping establish foundational AI engineering capabilities, operating standards, and a small, high-performing AI engineering team.
- Design, develop, and deploy enterprise AI and Generative AI applications for prioritized banking use cases (e.g., customer service, fraud detection, document processing, knowledge management, and operational efficiency)
- Architect LLM-enabled solutions spanning retrieval-augmented generation, vector search, agentic workflows, MCP, model orchestration, tool/function calling, and human-in-the-loop controls.
- Build production-grade services and APIs using Python, FastAPI or Flask, Azure OpenAI, Azure ML, Databricks, ADLS, and modern cloud-native patterns.
- Integrate AI capabilities into enterprise applications, developer workflows, knowledge management platforms, automation, analytics, and decision-support processes.
- Establish engineering practices for CI/CD, testing, model evaluation, observability, performance optimization, security, and responsible AI controls.
- Establish reusable AI engineering frameworks, reference architectures, code standards, deployment patterns, and governance controls to accelerate enterprise adoption.
- Partner with business, data, cybersecurity, risk, compliance, legal, and vendor teams to ensure solutions meet regulatory, privacy, auditability, and operational risk expectations.
- Prototype rapidly with stakeholders, convert pilots into scalable implementations, and define measurable adoption and impact metrics.
- Evaluate LLM platforms for accuracy, latency, cost, security, explainability, and fit for regulated enterprise use cases.
- Support hiring, mentoring, and day-to-day technical leadership of AI engineers and cross-functional delivery teams.
- Stay current with emerging AI technologies and advise leadership on practical opportunities, risks, and implementation tradeoffs.
- Perform other duties as assigned.
AI Fluency & Hands-On LLM Skills
- Hands-on experience with major LLM platforms, including OpenAI ChatGPT/Codex, Anthropic Claude, Google Gemini, Microsoft Copilot/Azure OpenAI, AWS Bedrock, and open-source models such as Llama or Mistral.
- Practical experience with prompt engineering, RAG, embeddings, vector databases, LLM orchestration frameworks, agentic workflows, evaluation frameworks, and hallucination mitigation.
- Ability to design AI applications that include data protection, source validation, access control, logging, monitoring, traceability, and human review where appropriate.
- Strong understanding of Responsible AI, model governance, prompt-injection risks, data privacy, and production controls for LLM-enabled solutions.
- Bachelor's degree in Computer Science, Engineering, Data Science, AI/ML, or equivalent practical experience; advanced degree preferred.
- 10+ years of progressive experience in software engineering, AI engineering, platform engineering or related technology leadership roles, including experience delivering production AI solutions
- Proven experience leading AI, data, automation, or emerging technology initiatives from strategy and experimentation through production delivery.
- Strong hands-on engineering background in Python, API design, microservices, cloud architecture, distributed systems, data pipelines, CI/CD, testing, observability, and secure software delivery.
- Deep experience with the Azure ecosystem, including Azure OpenAI, Azure ML, Databricks, ADLS, Azure AI Search, and related enterprise integration patterns.
- Experience with LLM frameworks and tooling such as LangChain, LlamaIndex, Semantic Kernel, vector databases, model registries, evaluation frameworks, and monitoring/observability tools.
- Strong process and data discipline, including data quality, lineage, metadata, workflow design, controls, operational risk, and measurable business outcomes.
- Experience in financial services, banking, fintech, insurance, or another regulated industry with strong understanding of compliance, auditability, risk management, and governance.
- Ability to lead cross-functional teams, influence senior stakeholders, mentor engineers, and translate complex AI capabilities into practical business solutions.
- Strong executive communication skills, including the ability to define AI roadmaps, operating models, standards, adoption plans, and success metrics.
Preferred Qualifications
- Master's degree in AI, Computer Science, Data Science, Engineering, or a related field.
- Experience establishing AI engineering teams, platforms, reusable delivery patterns, and enterprise AI standards.
- Experience with copilots, enterprise search, intelligent document processing, workflow automation, and AI-enabled knowledge management.
- Experience driving AI vendor evaluation and selection processes within regulated environments
- Familiarity with model risk management, third-party/vendor risk, privacy impact assessments, and regulated technology delivery.
- Experience with MLOps/LLMOps, AI monitoring, evaluation pipelines, model/prompt registries, and production incident management.
- Track record of mentoring senior engineers and building high-performing technical teams.
Applicants must have legal authorization to work in the United States. We do not offer visa sponsorship at this time.
AI Engineer Jobs by Experience Level
Top Cities Hiring AI Engineers
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Who's Hiring
- JPMorganChase180
- Google115
- OpenAI73
- Capital One58
- SpaceX54
Top Industries Hiring
- Technology & Software142
- Electronics & Hardware42
- Consulting & Professional Services23
- Investment & Asset Management19
- Law & Legal Services15
What Employers Look For
The qualifications that appear most often in AI engineer jobs.
- Proficiency in Python and at least one deep learning framework such as PyTorch or TensorFlow
- Experience designing, training, and deploying machine learning models in production environments
- Familiarity with cloud platforms such as AWS, Google Cloud, or Azure for ML workloads
- Understanding of LLM architectures, prompt engineering, and fine-tuning or RLHF techniques
- Bachelor's or master's degree in computer science, machine learning, statistics, or a related field
- Experience with MLOps tooling including experiment tracking, model versioning, and pipeline orchestration
Tips for Your AI Engineer Job Search
Tailor your resume to each stack
AI engineer job listings vary sharply by stack. A role focused on LLM fine-tuning calls out different tools than one built around real-time inference pipelines. Match your resume's skills section to the exact frameworks each posting names, whether that's PyTorch, JAX, or Ray.
Show models you shipped, not studied
Hiring managers scan for production signals: a model you deployed, latency you reduced, an evaluation benchmark you improved. Link to a GitHub repo, a paper, or a write-up that shows the problem, your approach, and a measurable result. Side projects count if they ran in production.
Apply early to roles that fit
Migrate Mate lists ai engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter openings by your ML domain
Generalist AI engineer titles often hide very narrow scopes: recommendation systems, speech models, or safety and alignment work. Read the responsibilities section, not just the title, to confirm the role sits in your area before you spend time on a tailored application.
Prepare for a system design round
Most senior ai engineer loops include an ML system design interview separate from coding. Practice scoping a training pipeline or an inference architecture end to end: data ingestion, feature engineering, model serving, and monitoring. Talk through tradeoffs explicitly rather than converging on one solution immediately.
Negotiate on compute and data access
Compensation for ai engineers often includes non-salary levers that matter for your work: GPU budget, access to proprietary datasets, and time allocated to research. Ask about these during the offer stage alongside equity and base, especially at startups where infrastructure budgets vary widely.
AI Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most ai engineers?
The companies hiring the most ai engineers right now include JPMorganChase, Google, and OpenAI, with the largest share of openings in California, Texas, and New York, based on current listings on Migrate Mate as of September 2026. Demand is concentrated at large technology companies, AI-native startups, and enterprise software firms expanding their AI product lines.
How many ai engineer jobs are remote?
About 79% of ai engineer openings are fully remote or hybrid as of September 2026, making it one of the more remote-accessible engineering roles. Sub-areas like LLM research, MLOps, and AI infrastructure tend to offer the highest share of remote arrangements, while roles tied to hardware, robotics, or on-site data pipelines are more likely to require in-person presence.
How do you become an ai engineer?
You become an ai engineer by building a foundation in linear algebra, probability, and Python, then working through machine learning fundamentals using hands-on projects. From there, specialize in an area such as NLP, computer vision, or MLOps, and build a portfolio of production-style work you can point to. A degree helps open doors, but demonstrated project experience and open-source contributions carry significant weight in hiring decisions.
How do you get hired as an ai engineer with little experience?
Focus on shipping something real: fine-tune an open-source model, build an end-to-end inference API, or contribute to an open-source ML library. Document what you built, what broke, and how you fixed it. Apply to roles with titles like ML engineer intern, junior AI engineer, or AI associate, which explicitly target early-career candidates. A strong project portfolio often outweighs years of experience at companies actively growing their AI teams.
What does the ai engineer interview process look like?
A typical ai engineer loop runs across several stages: an initial recruiter screen, a technical phone interview covering Python and ML fundamentals, a take-home or live coding assessment, and a full onsite or virtual loop. The loop usually includes a machine learning system design round, a coding round focused on data structures and algorithms, and a behavioral interview. Some companies add a paper discussion or a presentation of a past project.
Where can I find and apply to ai engineer jobs?
You can find and apply to ai engineer jobs on Migrate Mate, which lists current openings from across the United States in one place. Search for roles that match your specialization and experience level, then apply directly to each listing that fits.
See All 3,840+ AI Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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