Design and implement LLM-driven agent services on AWS for code, docs, tests and observability
Build orchestration and communication layers between agents using A2A, LangGraph or AutoGen
Integrate AI agents with Jira, Bitbucket, GitHub, Terraform and monitoring tools
Provide technical leadership and mentorship to junior engineers
Drive governance and adoption of approved AI-assisted engineering practices across teams
From the employer’s posting
Designs and Implement LLM-driven agent services for design, code generation, documentation, test creation and observability on AWS
Develops orchestration and communication layers between agents using frameworks like A2A SDK, LangGraph, or Auto Gen
Integrates AI agents with toolchains such as Jira, Bitbucket, Github, Terraform and monitoring platforms
Provides technical leadership, mentorship, and guidance to junior engineers and team members.
Drives adoption and governance of approved AI-assisted engineering practices across teams
Education & alternatives
Required qualifications, capabilities and skills
- BE/B. Tech, ME/MS or PhD degree in Computer Science, or Machine learning related field.
- Strong hands-on skills in Python, Pydantic, FastAPI, LangGraph, and Vector Databases for building RAG based AI agent solutions integrating with multi-agent orchestration frameworks and deploying end-to-end pipelines on AWS (EKS, Lambda, S3, Terraform)
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
Python
AWS
Azure
Docker
GCP
Kubernetes
S3
Terraform
FastAPI
Where the posting names them
- BE/B. Tech, ME/MS or PhD degree in Computer Science, or Machine learning related field.
- Strong hands-on skills in Python, Pydantic, FastAPI, LangGraph, and Vector Databases for building RAG based AI agent solutions integrating with multi-agent orchestration frameworks and deploying end-to-end pipelines on AWS (EKS, Lambda, S3, Terraform)
- Experience with LLMs integration, prompt/context engineering, AI Agent frameworks like Langchain/LangGraph, Autogen, MCPs, A2A.
Job Responsibilities
- Designs and Implement LLM-driven agent services for design, code generation, documentation, test creation and observability on AWS
- Develops orchestration and communication layers between agents using frameworks like A2A SDK, LangGraph, or Auto Gen
- Expert in at least one of the following areas: Natural Language Processing, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis.
- Solid understanding of Cloud (AWS/ Azure/ GCP) and DevOps (CI/CD, Terraform, Kubernetes, Docker and APIs
- Develops orchestration and communication layers between agents using frameworks like A2A SDK, LangGraph, or Auto Gen
- Integrates AI agents with toolchains such as Jira, Bitbucket, Github, Terraform and monitoring platforms
- Collaborates on system design, SDK development and data pipelines supporting agent intelligence
About 318,512 in 2025.Annual report (SEC Form 10-K)
Company
Listed as JPM.
Layoff notices
Notices this year and last cover 152 people. The latest was filed March 23, 2026.State WARN notices
Demand for AI Engineer roles
New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.
August 17Last week
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
As the next push into this investment, JPMC is hiring the best talents to join our AI engineering team. We are executing like a startup and building the next generation technology that combines JPMC unique data and full-service advantage to develop high impact AI applications and platforms in the financial services industry. We are looking for people who are excited about the opportunity.
As a Data Scientist Lead at JPMorgan Chase, you design and deliver trusted market-leading technology products in a secure, stable, and scalable way.
Job Responsibilities
Designs and Implement LLM-driven agent services for design, code generation, documentation, test creation and observability on AWS
Develops orchestration and communication layers between agents using frameworks like A2A SDK, LangGraph, or Auto Gen
Integrates AI agents with toolchains such as Jira, Bitbucket, Github, Terraform and monitoring platforms
Collaborates on system design, SDK development and data pipelines supporting agent intelligence
Provides technical leadership, mentorship, and guidance to junior engineers and team members.
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Required qualifications, capabilities and skills
BE/B. Tech, ME/MS or PhD degree in Computer Science, or Machine learning related field.
Strong hands-on skills in Python, Pydantic, FastAPI, LangGraph, and Vector Databases for building RAG based AI agent solutions integrating with multi-agent orchestration frameworks and deploying end-to-end pipelines on AWS (EKS, Lambda, S3, Terraform)
Experience with LLMs integration, prompt/context engineering, AI Agent frameworks like Langchain/LangGraph, Autogen, MCPs, A2A.
Deep knowledge in Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics.
Expert in at least one of the following areas: Natural Language Processing, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis.
Solid understanding of Cloud (AWS/ Azure/ GCP) and DevOps (CI/CD, Terraform, Kubernetes, Docker and APIs