Are you a senior Python engineer who wants to work at the intersection of data platforms and applied AI? This is your opportunity to join a small, high-impact team building something new from the ground up — where your decisions shape the architecture, not just the backlog. At JPMorganChase, we invest in engineers who are curious, pragmatic, and ready to grow into emerging technology stacks.
As a Senior Lead Software Engineer at JPMorganChase within the Corporate Technology Data and Analytics Services team, you will be a founding contributor to the Context Plane — a greenfield platform that connects the firm's data mesh and knowledge sources to AI agents and large language model tools. You will own components end-to-end, from ingestion pipelines to governed retrieval services, and your engineering instincts will directly influence how the platform evolves. This is a hands-on senior role with real architectural scope, active cross-functional collaboration, and strong support for internal mobility and upskilling.
Job responsibilities
- Design, build, and maintain backend services and data pipelines in Python that load firm knowledge into a knowledge graph and vector store
- Build and evolve the serving layer — including graph and vector retrieval, GraphRAG, response assembly, and a Model Context Protocol endpoint consumed by downstream agents
- Extract and promote reusable components into a shared core library, reducing duplication across the platform's repositories
- Integrate with data sources and services across the firm, including enterprise AI and large language model gateways
- Own quality across your components: automated testing, code reviews, observability, and resilient, secure service design
- Partner with Corporate Technology AI, product, and data science colleagues to translate concrete use cases into working, measurable capabilities
- Contribute to design discussions and agile ceremonies, and actively mentor teammates to raise the engineering bar across the team
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), 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 advanced applied experience
- Demonstrated expertise building production-grade backend services and data pipelines in Python
- Strong command of API design principles (e.g., FastAPI), automated testing, CI/CD practices, and source control workflows
- Experience designing and building data ingestion or integration pipelines at scale, with attention to data quality and resilience
- Proficiency working with cloud infrastructure (AWS) and containerized services (Docker/ECS)
- Ability to own technical components end-to-end — from design through deployment and observability
- Strong collaboration skills with the ability to work across engineering, product, and data science disciplines
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
Preferred qualifications, capabilities, and skills
- Experience with graph databases and Cypher query language (e.g., Neo4j) or a strong interest in graph data modeling
- Familiarity with vector search, embeddings, or retrieval-augmented generation (RAG) patterns
- Exposure to large language model serving, agentic patterns (tool/function calling, Model Context Protocol), or platforms such as Bedrock or Azure OpenAI
- Experience with Databricks, MongoDB, or large-scale extract, transform, and load / data integration workflows
- Knowledge of data governance, lineage, and entitlements concepts in an enterprise environment