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