Lead Software Engineer Applied AI Engineer (Agentic Gen AI)

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Lead Software Engineer at JPMorganChase within Commercial & Investment Bank you are an integral part of an agile team that works to enhance build and deliver trusted market-leading AI-enabled technology products in a secure stable and scalable way. You will own the end-to-end lifecycle of AI systems from rapid prototyping and prompt engineering through to production deployment monitoring and continuous improvement. You will apply deep technical expertise in agentic architectures LLM orchestration and modern AI frameworks to tackle a diverse array of challenges that span multiple technologies and applications.

Job Responsibilities:

• Build and productionize agentic AI solutions including agents orchestrators tool/function integrations workflow/state management and guardrails
• Design and develop Python and Java services (microservices and shared libraries) with strong API contracts and domain-driven design where applicable
• Architect and manage data pipelines embeddings and vector stores that power RAG and other AI capabilities including prompt versioning templating and optimization for reliability
• Build evaluation and observability frameworks to monitor AI system performance including hallucination detection latency accuracy and user feedback loops
• Deliver AI-enabled business UI experiences in partnership with product and UX ensuring usability performance and accessibility
• Establish and maintain MLOps practices for AI model deployment including CI/CD pipelines model versioning and infrastructure-as-code for AI workloads
• Collaborate with security risk and controls partners to ensure solutions meet governance and compliance expectations for AI-enabled systems

• Drive decisions that influence product design application functionality and technical operations applying creative problem-solving to tackle challenges beyond routine approaches

Required qualifications capabilities and skills:

• Formal training or certification on software engineering concepts and 8 years of applied experience in software engineering with demonstrable depth in AI/ML systems
• Advanced proficiency in Python with strong experience in at least one additional programming language (e.g. Java)
• Hands-on experience building and productionizing agentic AI systems including multi-agent orchestration using frameworks such as LangChain LangGraph or equivalent
• Practical understanding of LLM orchestration retrieval-augmented generation (RAG) tool calling prompt engineering and dynamic reasoning
• Experience evaluating and integrating AI/LLM capabilities into production applications including model evaluation output quality monitoring and feedback loops
• Hands-on experience with AI coding tools (Claude Code Copilot or similar) you know when they accelerate work and when human judgment is critical
• Hands-on practical experience delivering system design application development testing and operational stability
• Strong experience with cloud services (AWS)

Preferred qualifications skills and capabilities:

• Experience architecting solutions where AI tools handle implementation while you focus on business logic and edge cases
• Track record of rapid prototyping and iteration in ambiguous problem spaces
• Experience building with LLMs as development partners not just API integrations
• Proficiency in graph database query languages such as Cypher Gremlin or SPARQL
• Knowledge of database technologies such as SQL NoSQL and ORM frameworks
• Experience with MLOps tooling model versioning and AI deployment pipelines
• Familiarity with vector databases (e.g. OpenSearchFAISSDB) and embedding management