Context Plane Python Engineer

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