We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan Chase within the Chief Data and Analytics Office, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

Execute creative software solutions, design, development, and technical troubleshooting for AI‑enabled applications. Develop secure, high‑quality production code; review and debug SDK and service integrations. Identify and automate remediation of recurring issues to improve reliability of AI features and services. Lead evaluation sessions with vendors and internal teams on model capabilities, safety, and integration patterns. Lead communities of practice to share prompt engineering, evaluation methods, and SDK best practices. Add to team culture of diversity, opportunity, inclusion, and respect. Build and ship AI‑powered features using Vertex AI base models (e.g., Gemini), including prompt design, function calling, and SDK/REST integrations. Fine‑tune and deploy models; manage Model Registry and rollout across environments (online/batch inference). Implement input/output safety (guardrails, moderation) and comprehensive LLM usage logging/monitoring. Integrate DialogFlow API and Vertex AI Search with grounding to reliable sources and source attribution.

 

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experience. Hands‑on experience delivering system design, application development, testing, and operational stability for AI services. Advanced proficiency in Python plus one of Java/Go/TypeScript for service integration and tooling. Proficiency in automation and continuous delivery methods; strong unit/integration testing discipline. Proficient in all aspects of the Software Development Life Cycle and secure coding practices. Advanced understanding of CI/CD, application resiliency, and security for AI applications. Practical cloud‑native experience (GCP) and microservices/API design. Demonstrated experience with Vertex AI SDKs/REST APIs, prompt engineering, and model evaluation. Experience with model fine‑tuning, deployment, and model registry operations (online/batch).

Ability to implement AI guardrails/safety filters and configure LLM usage logging/monitoring.

 

Preferred qualifications, capabilities, and skills

Experience with Vertex AI Search, Vector Search, and RAG patterns for grounding and retrieval. Familiarity with DialogFlow conversational orchestration and enterprise integration. Strong API design, eventing, and observability (metrics, tracing, logging) for AI services. Knowledge of Gemini model families and safety configuration options. Understanding of cost management and spend alerting for AI workloads. Familiarity with Florence/Atlas 2.0 enablement roadmap and controls.