Software Engineer III - AI/ML Engineer

JPMorgan Chase & Co.•Jersey City, NJ

8h•$137,750 - $185,000

About The Position

As a Software Engineer III at JPMorgan Chase within Corporate Technology, you will help design and deliver agentic AI platforms and large language model-enabled services for Finance use cases. You will contribute to technical design, build cloud-native services on AWS, and improve system quality through evaluation and observability. You will help raise engineering standards through strong code reviews, documentation, and collaboration across teams. The role involves building production-grade AI systems and reusable enterprise services, optimizing AI workloads, and integrating AI capabilities with enterprise platforms and workflows. You will work with product, platform, and engineering partners to turn requirements into resilient, measurable deliverables and contribute to technical standards and team practices. You will also use enterprise-authorized AI-assisted development tools, validate their outputs, and promote secure and responsible use of these tools. Chase is a leading financial services firm whose mission is to create engaged, lifelong customer relationships. Its Consumer & Community Banking division provides services including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans, and payment processing.

Requirements

Formal training or certification in software engineering concepts and 3+ years of applied experience.
Hands-on experience building and operating production large language model applications, including agentic patterns and tool integrations.
Strong software engineering fundamentals and ability to deliver cloud-native services using containers and serverless designs on AWS.
Advanced Python programming skills and experience writing production-quality code.
Experience with retrieval-augmented generation approaches, including embeddings and semantic search, and practical context engineering.
Proficiency building React-based front-end experiences that integrate backend APIs, with strong attention to reliability, security, and performance.
Experience establishing or contributing to evaluation, testing, and monitoring practices for AI system quality and reliability.
Ability to troubleshoot complex issues across distributed systems, including asynchronous workflows and event-driven architecture.
Strong collaboration skills and ability to communicate technical decisions and trade-offs clearly to partners.
Hands-on experience using enterprise-authorized AI-assisted software development tools for coding, test creation, troubleshooting, or documentation, with ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs and outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.

Nice To Haves

Experience deploying and operating workloads on Kubernetes-based platforms and container orchestration patterns.
Experience with experimentation frameworks and automated regression testing for large language model quality.
Familiarity with large language model cost governance and performance optimization techniques, such as caching and context efficiency.
Experience implementing guardrail patterns that support safe, reliable AI behavior in production.
Experience building reusable platform components and reference implementations adopted by multiple teams.

Responsibilities

Design and implement components of scalable, reliable agentic AI platforms for enterprise workflows.
Build production-grade AI systems, including agents, skills, memory patterns, guardrails, and tool-use orchestration.
Implement retrieval and context-engineering patterns, including embeddings, semantic search, grounding, summarization, and prompt/version management.
Engineer cloud-native services on AWS using containers, serverless compute, and event-driven messaging patterns.
Optimize latency, throughput, scalability, caching, context efficiency, and cost across large language model workloads.
Develop secure, reusable APIs and integrations that connect AI capabilities to enterprise platforms and workflows.
Implement evaluation, experimentation, regression testing, and observability signals to improve quality and agent behavior over time.
Partner with product, platform, and engineering teams to translate requirements into resilient, measurable deliverables.
Contribute to technical standards and code quality through design reviews, documentation, and peer code reviews.
Use enterprise-authorized AI coding-assist tools to improve code quality, delivery speed, and productivity, validating outputs through peer review, automated testing, and secure coding standards.
Contribute learnings and reusable patterns to improve broader team effectiveness.
Apply knowledge of tools within the Software Development Life Cycle toolchain, including authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Benefits

Competitive total rewards package, including base salary determined by role, experience, skill set, and location.
Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
Comprehensive health care coverage.
On-site health and wellness centers.
Retirement savings plan.
Backup childcare.
Tuition reimbursement.
Mental health support.
Financial coaching.
Additional benefits and programs based on eligibility.