Build and own backend services and Python data pipelines to load firm knowledge into knowledge graphs and vector stores. Implement retrieval and serving layers (graph/vector retrieval, GraphRAG, Model Context Protocol), integrate with enterprise data and LLM gateways, ensure testable, observable, resilient services, drive reuse and CI/CD, collaborate with product and data science, mentor teammates, and apply enterprise-authorized AI-assisted engineering practices securely.
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
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Similar Jobs
Financial Services
Designs, develops, and supports the Trade Execution Platform; writes secure production code; troubleshoots complex issues; produces architecture and design artifacts; leverages AI-assisted coding tools responsibly; applies SDLC, CI/CD, resiliency, and security best practices; analyzes data to drive software improvements.
Top Skills:
Ai-Assisted Development ToolsAWSCi/CdJavaPythonSQL
Financial Services
Lead an agile engineering team building secure, scalable AI & ML data platform products. Provide technical direction, write and review production-quality code, drive architecture and operational best practices, champion enterprise AI-assisted development and responsible AI use, and promote reuse and engineering standards across the organization.
Top Skills:
Ai-Assisted Development ToolsAsynchronous MessagingAutomated TestingAWSCachingCi/CdCloud NativeContainer OrchestrationInfrastructure-As-CodeJavaMicroservicesNoSQLReactRelational DatabasesRestful ApisServerlessSpring BootTypescript
Financial Services
Lead and mentor engineering teams to design, build, and operate secure, scalable AI & ML data platform solutions. Produce and review production-quality code, drive architectural decisions, adopt enterprise AI-assisted development practices, and promote secure, resilient cloud-native operations and automation across the SDLC.
Top Skills:
Artificial IntelligenceAsynchronous MessagingAutomated Testing FrameworksAWSCachingCi/CdCloud NativeContainer OrchestrationEnterprise Ai-Assisted Development ToolsInfrastructure-As-CodeJavaMachine LearningMicroservicesNoSQLReactRelational DatabasesRestful ApisServerless ComputingSpring BootTypescript
What you need to know about the Edinburgh Tech Scene
From traditional pubs and centuries-old universities to sleek shopping malls and glass-paneled office buildings, Edinburgh's architecture reflects its unique blend of history and modernity. But the fusion of past and future isn't just visible in its buildings; it's also shaping the city's economy. Named the United Kingdom's leading technology ecosystem outside of London, Edinburgh plays host to major global companies like Apple and Adobe, as well as a growing number of innovative startups in fields like cybersecurity, finance and healthcare.
