Lead Software Engineer - AI

Build the context backbone for governed, agentic software delivery by designing secure, scalable Code Intelligence capabilities that help engineers navigate and act on code in real time.

As a Lead Software Engineer - AI at JPMorganChase within the Consumer & Community Banking Code Intelligence team, you will be a hands-on technical lead building the access, relevance, and trust layers that enable AI-driven developer workflows to scale safely.

You will design and deliver distributed systems that combine retrieval, ranking, and security controls to accelerate engineering outcomes across a large, complex codebase.

Job responsibilities


* Design and deliver an entitlements model that ensures code context and AI responses respect repository-level read permissions and access boundaries, enabling safe scale-up beyond pilot.


* Build context prioritization and relevance ranking scoped to user context (for example, team ownership and service affinity) to improve response quality and reduce cost per query at scale.


* Implement trust and quality signals for AI output, including grounding, citation accuracy, and freshness metrics, establishing measurable standards for governed autonomy.


* Architect and develop production-grade services on AWS (for example, containers, serverless, queues, search, and relational data stores), applying best practices for scalability, observability, and cost efficiency.


* Own deep cross-system debugging and integration across event-driven ingestion, indexing, retrieval, and serving pathways.


* Drive technical design reviews and mentor engineers through hands-on leadership, clear standards, and strong engineering judgment.


* Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes, while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.


* Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Required qualifications, capabilities, and skills


* Formal training or certification on software engineering concepts and 5+ years applied experience.


* Hands-on experience designing and delivering distributed systems in a production AWS environment.


* Proficiency with retrieval-augmented generation architectures, including chunking strategies, embeddings, and vector + lexical search (for example, OpenSearch, pgvector, FAISS).


* Experience building and operating agentic tooling, such as tool-use patterns with large language models, agent orchestration frameworks, or MCP-based integrations.


* Proficiency in Java or Python and experience with event-driven architectures (for example, SQS, SNS, Kafka).


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