AI ML Platform Jobs
AI ML Platform jobs are open across technology, finance, healthcare, and defense, from new-grad to staff and principal engineer, with specializations in MLOps, model serving infrastructure, and distributed training pipelines. Find a role that fits from the openings below and apply directly.
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Help shape how teams across the firm build and deploy agentic artificial intelligence products—at scale, with quality, and with measurable impact. You will work on a high-visibility platform that accelerates engineering teams, raises delivery standards, and turns complex business needs into reliable production outcomes. Join a team where strong engineering, thoughtful collaboration, and continuous learning are core to how we operate.
As a Applied AI and Machine Learning Lead - Agent Builder Platform at JPMorganChase within Enterprise Technology, AI and Machine Learning & Data Platforms, you will lead the technical design and delivery of agentic AI products and platform capabilities used by engineering teams across the organization. You will translate high-impact business problems into production-grade solutions, from discovery and design through deployment and ongoing operations. You will set a high engineering quality bar while partnering closely with product and stakeholder groups to deliver measurable outcomes.
Job responsibilities
- Lead end-to-end delivery of agentic AI and large language model-powered use cases from problem framing and technical design through production deployment and monitoring.
- Own core platform services and reusable components that enable teams to build, evaluate, and operate AI agents safely at scale.
- Establish engineering standards through hands-on system design, rigorous code review, and mentorship to improve reliability, maintainability, and developer experience.
- Build and operationalize evaluation, testing, and observability capabilities (tracing, metrics, logs, and analytics) to continuously improve solution quality.
- Implement robust safety and governance patterns, including guardrails, access controls, and audit-ready operational practices aligned to enterprise expectations.
- Partner with product managers and stakeholders to shape roadmaps, define success metrics, and prioritize work that delivers measurable business impact.
- Drive cross-functional alignment across engineering, data, security, and risk partners to ensure solutions are secure, stable, and scalable.
- Contribute to technical documentation, reference implementations, and enablement content that accelerates adoption and responsible usage.
Required qualifications, capabilities and skills
- Formal training or certification on applied artificial intelligence and machine learning concepts and 5+ years applied experience
- Advanced proficiency in Python with strong software engineering fundamentals, including testing, design patterns, version control, and code review practices.
- Hands-on experience building, evaluating, and deploying machine learning or large language model-enabled systems into production environments.
- Practical experience with prompt engineering and retrieval-augmented generation, including evaluation methods and quality measurement.
- Experience designing and operating reliable services, including incident response readiness, performance tuning, and operational stability for data-intensive systems.
- Demonstrated ability to lead technical decisions and deliver outcomes through ambiguity, balancing speed, risk, and long-term maintainability.
- Strong communication skills with the ability to explain technical trade-offs to both technical and non-technical stakeholders.
Preferred qualifications, capabilities and skills
- Experience with agent orchestration frameworks (for example, LangGraph, LlamaIndex, Google ADK or custom orchestration) and evaluation tooling for large language model systems.
- Experience with continuous integration and continuous delivery practices and containerization (Docker and Kubernetes) for production deployments.
- Familiarity with vector databases, embedding pipelines, or graph-based memory approaches used in retrieval-augmented generation solutions.
- Experience with cloud and machine learning platforms (for example, Amazon Web Services, Databricks, or comparable platforms).
- Experience contributing to AI governance, validation approaches, or guardrail frameworks in enterprise settings.
#LI-RB3
ABOUT USWe offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
Help shape how teams across the firm build and deploy agentic artificial intelligence products—at scale, with quality, and with measurable impact. You will work on a high-visibility platform that accelerates engineering teams, raises delivery standards, and turns complex business needs into reliable production outcomes. Join a team where strong engineering, thoughtful collaboration, and continuous learning are core to how we operate.
As a Applied AI and Machine Learning Lead - Agent Builder Platform at JPMorganChase within Enterprise Technology, AI and Machine Learning & Data Platforms, you will lead the technical design and delivery of agentic AI products and platform capabilities used by engineering teams across the organization. You will translate high-impact business problems into production-grade solutions, from discovery and design through deployment and ongoing operations. You will set a high engineering quality bar while partnering closely with product and stakeholder groups to deliver measurable outcomes.
Job responsibilities
- Lead end-to-end delivery of agentic AI and large language model-powered use cases from problem framing and technical design through production deployment and monitoring.
- Own core platform services and reusable components that enable teams to build, evaluate, and operate AI agents safely at scale.
- Establish engineering standards through hands-on system design, rigorous code review, and mentorship to improve reliability, maintainability, and developer experience.
- Build and operationalize evaluation, testing, and observability capabilities (tracing, metrics, logs, and analytics) to continuously improve solution quality.
- Implement robust safety and governance patterns, including guardrails, access controls, and audit-ready operational practices aligned to enterprise expectations.
- Partner with product managers and stakeholders to shape roadmaps, define success metrics, and prioritize work that delivers measurable business impact.
- Drive cross-functional alignment across engineering, data, security, and risk partners to ensure solutions are secure, stable, and scalable.
- Contribute to technical documentation, reference implementations, and enablement content that accelerates adoption and responsible usage.
Required qualifications, capabilities and skills
- Formal training or certification on applied artificial intelligence and machine learning concepts and 5+ years applied experience
- Advanced proficiency in Python with strong software engineering fundamentals, including testing, design patterns, version control, and code review practices.
- Hands-on experience building, evaluating, and deploying machine learning or large language model-enabled systems into production environments.
- Practical experience with prompt engineering and retrieval-augmented generation, including evaluation methods and quality measurement.
- Experience designing and operating reliable services, including incident response readiness, performance tuning, and operational stability for data-intensive systems.
- Demonstrated ability to lead technical decisions and deliver outcomes through ambiguity, balancing speed, risk, and long-term maintainability.
- Strong communication skills with the ability to explain technical trade-offs to both technical and non-technical stakeholders.
Preferred qualifications, capabilities and skills
- Experience with agent orchestration frameworks (for example, LangGraph, LlamaIndex, Google ADK or custom orchestration) and evaluation tooling for large language model systems.
- Experience with continuous integration and continuous delivery practices and containerization (Docker and Kubernetes) for production deployments.
- Familiarity with vector databases, embedding pipelines, or graph-based memory approaches used in retrieval-augmented generation solutions.
- Experience with cloud and machine learning platforms (for example, Amazon Web Services, Databricks, or comparable platforms).
- Experience contributing to AI governance, validation approaches, or guardrail frameworks in enterprise settings.
#LI-RB3
ABOUT USWe offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
AI ML Platform Jobs by Experience Level
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Find AI ML Platform JobsAI ML Platform Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software12
- Insurance10
- Electronics & Hardware8
- Retail1
- Fintech1
What Employers Look For
The qualifications that appear most often in AI ML platform jobs.
- Proficiency in Python and experience building or maintaining ML pipelines at scale
- Hands-on experience with containerization and orchestration tools such as Docker and Kubernetes
- Familiarity with at least one managed ML platform such as SageMaker, Vertex AI, or Azure ML
- Experience designing or operating distributed training and model serving infrastructure
- Understanding of CI/CD principles applied to model training, evaluation, and deployment workflows
- Bachelor's or master's degree in computer science, engineering, or a closely related quantitative field
Tips for Your AI ML Platform Job Search
Quantify your infrastructure impact clearly
Hiring managers want to see throughput, latency, or cost numbers tied to work you shipped. Replace vague descriptions like 'improved model deployment' with concrete outcomes such as reduced pipeline runtime or cut cloud spend on inference workloads.
Separate MLOps from software engineering roles
AI ML platform openings split into infrastructure-heavy and research-adjacent tracks. Read job descriptions carefully for keywords like Kubeflow, Ray, or Triton versus SageMaker or Vertex AI to apply to roles that actually match your stack and experience level.
Apply early to roles that fit
Migrate Mate lists ai ml platform openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Build a public artifact before your system design round
AI ML platform interviews almost always include a system design exercise. Pushing a reproducible training pipeline or a feature store prototype to GitHub before you interview gives you a real example to reference when describing architectural trade-offs.
Tailor your cover letter to the orchestration stack
Most teams list their orchestration tools in the job description. Mentioning Airflow, Prefect, or Argo Workflows by name, with context on how you used them, signals you'll ramp faster than candidates who write generic platform experience statements.
Negotiate scope alongside compensation
In AI ML platform roles, ownership of the platform roadmap varies widely between companies. Ask during the offer stage which components the team controls end-to-end versus which are handed off to data science or DevOps, so you know the actual scope before accepting.
AI ML Platform Jobs: Frequently Asked Questions
Which companies are hiring the most ai ml platforms?
The companies hiring the most ai ml platforms right now include GEICO, Apple, and Nuro, with the largest share of openings in California, Washington, and New York, based on current listings on Migrate Mate as of August 2026. Demand is concentrated in companies scaling inference infrastructure or moving model development from research into production.
How many ai ml platform jobs are remote?
About 63% of ai ml platform openings are fully remote or hybrid as of August 2026, reflecting strong demand for distributed engineering talent. Model serving, pipeline observability, and feature engineering sub-roles tend to be the most remote-friendly, while roles involving on-premise GPU cluster management are more likely to require on-site presence.
How do you become a ai ml platform?
Start by building a solid foundation in Python, distributed systems, and at least one cloud provider. Work on end-to-end ML pipeline projects, even personal ones, to develop hands-on experience with orchestration, versioning, and model deployment. Contributing to open-source MLOps tools strengthens your portfolio. Moving into the role often means transitioning from a software engineering or data engineering background while picking up ML-specific tooling on the job.
Can you get an ai ml platform job with little experience?
Yes, entry-level ai ml platform roles exist, particularly at companies building out their platforms for the first time. Focus on demonstrating working knowledge of a pipeline orchestration tool, containerization basics, and a completed end-to-end project. Applying to smaller companies or startups where the platform team is early-stage gives you a better chance of being evaluated on potential rather than years of experience.
What does the ai ml platform interview process look like?
Most ai ml platform interviews include a recruiter screen, a technical phone interview covering Python and systems fundamentals, and an on-site or virtual loop with a machine learning system design round, a coding exercise focused on data structures or distributed concepts, and a cross-functional interview with data scientists or product managers. Some companies also include a take-home that asks you to design or debug a pipeline component.
Where can I find and apply to ai ml platform jobs?
You can find and apply to ai ml platform jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your experience and specialization, then apply directly to each one that fits. New openings are added regularly, so checking back frequently gives you access to roles as soon as they're posted.
See All 56 AI ML Platform Jobs
Find roles that match your experience and apply in just a few clicks.
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