AI Engineer - Sr Lead Software Engineer

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As aSenior Lead Software EngineeratJPMorgan

Chasewithinthe AI/ML Data Platforms team, you will be a key member of an agile team responsible for enhancing, building, and delivering trusted, market-leading technology products in a secure, stable, and scalable manner. You will drive meaningful business impact through hands-on engineering leadership, applying deep technical expertise and structured problem-solving to address complex challenges across multiple technologies and applications. In this role, you will help design and deliveragentic AI platformsandlarge language model (LLM)-enabled services for enterprise use cases.

You will contribute to architecture and engineering decisions, buildcloud-native services on AWS, and improve system quality through strongevaluation,observability, and operational excellence practices. You will also raise engineering standards through high-quality code reviews, clear documentation, and effective collaboration across teams.

• Provide technical guidance and direction to business and engineering teams by partnering with external teams to align on priorities, unblock delivery, and drive successful engineering outcomes.
• Develop secure, high-quality production code and lead code reviews; review, debug, and improve code written by others to raise overall engineering quality.
• Drive architecture and design decisions that influence product design, application functionality, and technical operations (including SDLC practices).
• Serve as a subject matter expert in one or more focus areas, helping teams make sound technical trade-offs and resolve complex problems.
• Evaluate and introduce leading-edge technologies where appropriate, influencing peers and decision-makers with clear rationale and risk/benefit analysis.
• Build and operate production-grade 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 strong 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.

• 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.

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

• 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…