AI Engineer - Sr Lead Software Engineer

JPMorganChase is hiring a Senior Lead Software Engineer to join the AI/ML Data Platforms team in Jersey City, NJ (onsite). This role focuses on hands-on engineering to design and deliver agentic AI platforms and LLM-enabled, cloud-native services on AWS, with an emphasis on production readiness, evaluation, observability, and elevating engineering standards through practical collaboration.

What you’ll do

• Provide technical guidance and direction to business and engineering teams by partnering with external teams to align on priorities, remove blockers, and drive successful delivery outcomes.
• Build and maintain secure, production-grade Python and lead code reviews; review and improve others’ code to raise overall engineering quality.
• Make architecture and design decisions that shape product design, application functionality, and technical operations, including SDLC practices.
• Act as a subject matter expert in one or more focus areas, supporting sound technical trade-offs and resolution of complex problems.
• Evaluate and introduce advanced technologies when appropriate, providing peers and decision-makers with clear rationale and risk or benefit analysis.
• Develop and operate production LLM applications, including agentic patterns and tool integrations for enterprise use cases.
• Design and deliver cloud-native services on AWS using containers and serverless architectures, with attention to scalability and operational resilience.
• Implement retrieval-augmented generation (RAG) solutions, including embeddings, semantic search, and practical context engineering to improve answer quality and control.
• Build reliable service APIs and integrations with a focus on security, performance, and maintainability.
• Drive adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (including AI-assisted code review or refactoring, test acceleration, release readiness, and incident/root-cause analysis). Establish measurable validation standards such as secure coding, peer review, and automated testing.
• Apply Software Development Life Cycle toolchain knowledge, including approved AI-assisted development and automation capabilities, to scale automation value.

What you bring

• 5+ years of applied experience, along with formal training or certification on software engineering concepts.
• Strong Python engineering skills, with experience in PyTorch or TensorFlow.
• Expertise working with vector storage systems and designing memory for agents.
• Proven experience developing long-running agents that autonomously use tools, skills, and human-in-the-loop patterns.
• Demonstrated production deployment of LLM-backed services such as APIs and microservices.
• Deep MLOps experience, including CI/CD, monitoring, incident response, and model governance.
• Cloud-native AI deployment experience on AWS or Azure, including cost and performance optimization.
• Commitment to responsible AI practices and operational excellence.
• Strong communication and collaboration skills across product, risk, legal, and compliance teams.
• Experience leading adoption of agentic AI-enabled development practices using enterprise-authorized tools, including standards for human-in-the-loop validation, auditability or traceability of changes, and secure handling of sensitive data.
• Knowledge of responsible AI use and control expectations in engineering workflows, including security and resiliency implications, data sensitivity, and risk-based governance. Ability to influence senior technical leaders on safe scaling patterns and reuse.

Technologies you’ll work with

Python, PyTorch, TensorFlow, vector storage systems, LLM-backed services, APIs, microservices, CI/CD, AWS, Azure, containers, serverless architectures, retrieval-augmented generation (RAG), embeddings, semantic search, SDLC, TLM, model governance

Preferred qualifications

• Experience with fine-tuning, adapters, or custom evaluation frameworks.
• Background operating AI systems in regulated environments (finance, healthcare, etc.).
• Experience with prompt engineering and LLM orchestration.
• Knowledge of safety filters, audit logging, and explainability in production systems.
• Experience mentoring senior engineers and leading architecture discussions.
• Demonstrated ability to influence technical roadmaps and priorities.

Compensation: USD 175,750 - 260,000 per year.