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 a Senior Lead Software Engineer at JPMorganChase within the 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 deliver agentic AI platforms and large language model (LLM)-enabled services for enterprise use cases.

You will contribute to architecture and engineering decisions, build cloud-native services on AWS, and improve system quality through strong evaluation, observability, and operational excellence practices.

You will also raise engineering standards through high-quality code reviews, clear documentation, and effective collaboration across teams.

Job responsibilities


* 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...