Description

As a Senior Software Engineer at JPMorgan Chase within Chase AI, you will design and build critical technology solutions across Chase AI components - including Chase Agent, Agent Operating Memory, Domain Agents, Agentic Experience Service, and Assurance.

Job Responsibilities:

• Execute end‑to‑end software solutions-design, development, and advanced troubleshooting-using non‑routine approaches to solve complex production and systems problems with measurable reliability improvements.
• Deliver secure, high‑quality production code and maintain algorithms that operate synchronously with dependent systems and SLAs, emphasizing performance, resiliency, and code quality.
• Define and produce architecture and design artifacts (e.g., ADRs, sequence diagrams, capacity models) for complex applications, ensuring software implementations meet performance, availability, cost, and compliance constraints.
• Use large, diverse data sets and telemetry to build visualizations and reporting that drive continuous improvement; proactively detect hidden issues and patterns to improve coding hygiene and system architecture.
• Collaborate across product, design, and operations to deliver end‑to‑end solutions, translating requirements into milestones and aligning roadmaps, OKRs, and outcomes.
• Champion engineering excellence and innovation-contribute to communities of practice, evaluate emerging technologies (including agentic solutions), and share learnings through talks, RFCs, and adoption playbooks.
• Foster an inclusive team culture grounded in diversity, opportunity, inclusion, and respect, while ensuring adherence to security, privacy, and regulatory requirements throughout the SDLC.

Required Qualifications

• 3+ years in software engineering, including 2+ years building scalable systems; experience with LLM/SLM agentic systems is a strong plus. and Clear communication, agile delivery, and strong documentation for mixed technical/non-technical audiences.
• MCP (plus): Experience setting up/maintaining MCP servers and building MCP-compatible tools/adapters.
• Tech stack: Strong in Java and Python; able to work across codebases. Production experience with Spring Boot (Spring AI/Security/Cloud) or Python (FastAPI/Flask).
• Engineering practices: Typed APIs/contracts, solid testing, and reliable packaging/release practices.
• Cloud/DevOps: Strong AWS, Docker, ECS/EKS, and CI/CD (GitHub Actions/Jenkins/CodeBuild).
• APIs/Observability: Strong REST/OpenAPI design (gRPC is a plus); familiarity with Splunk/CloudWatch/Prometheus/Grafana/OpenTelemetry.
• LLM engineering: Tools/function-calling, RAG, prompt/version management, context/token budgeting, safety guardrails; strong testing culture (unit/integration/load + agent evals).

Preferred Qualifications :

• Expertise with distributed orchestration patterns for LLM applications (graph-based flows, retries, fallbacks, guardrails) and secure integration with enterprise tools and data.
• Experience with safe rollout strategies (shadowing, A/B testing, progressive exposure), human-in-the-loop review, and continuous evaluation for quality and safety, including canary rollouts.
• Knowledge of API gateways, service mesh, and multi-region high availability and disaster recovery for mission-critical services.
• Familiarity with data privacy, security best practices, and regulatory compliance in financial services.
• Experience with performance optimization, scalability, and reliability engineering for large-scale systems.
• Ability to evaluate and integrate third-party tools, libraries, and frameworks to accelerate development.