AI Engineer - Sr Lead Software Engineer
Apply on JP Morgan Chase's site
Description -----------
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 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.
Required Qualifications, Capabilities, and Skills:
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Strong Python engineering skills; experience with PyTorch or TensorFlow
- 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.
Preferred Qualifications, Capabilities, and Skills:
- 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.
About JP Morgan Chase
JP Morgan Chase is a global financial services provider that offers investment banking, asset management, treasury, and other services.
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