What are the responsibilities and job description for the Sr Lead Software Engineer - Agentic AI Systems position at JPMorgan Chase?
Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Senior Lead Software Engineer at JPMorganChase within the Consumer and Community - Operations Technology, 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
As a lead for Agentic AI Systems, you are responsible to architect, build, and scale autonomous AI agent systems that operate with minimal human intervention. This role is responsible for driving the technical vision, design, and delivery of agentic AI platforms that perceive, reason, plan, and act across complex workflows. You will lead and influence engineers across the organization and collaborate cross-functionally to deliver production-grade AI agent systems and infrastructure.
Job responsibilities
- Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
- Develops secure and high-quality production code, and reviews and debugs code written by others
- Drives decisions that influence the product design, application functionality, and technical operations and processes
- Serves as a function-wide subject matter expert in one or more areas of focus
- Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
- Influences peers and project decision-makers to consider the use and application of leading-edge technologies
- Adds to the team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5 years applied experience. In addition, demonstrated coaching and mentoring experience
- 8 years of software engineering experience, with 3 years focused on AI/ML systems and at least 2 years in a technical lead, staff, or principal-level individual contributor role.
- Deep hands-on experience building agentic AI systems, including multi-agent orchestration, tool-use chains, planning/reasoning loops, and memory architectures.
- LLM Proficiency: Strong working knowledge of large language models (GPT-4 , Claude, Gemini, Llama, Mistral) including prompt engineering, fine-tuning, and evaluation methodologies.
- Expert-level proficiency in Python; strong experience with frameworks such as LangChain, LangGraph, AutoGen, CrewAI, Google ADK or equivalent.
- Experience with cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), and CI/CD pipelines for ML/AI workloads.
- Proficiency with vector databases (Pinecone, Weaviate, Qdrant, pgvector), embedding models, and RAG architectures.
- Strong foundation in distributed systems, API design, microservices, and event-driven architectures.
- Ability to tackle design and functionality problems independently with little to no oversight
- Experience in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field
Preferred qualifications, capabilities, and skills
Experience deploying autonomous agents in production at scale (enterprise or consumer-facing).
Familiarity with reinforcement learning from human feedback (RLHF) and reward modeling for agent alignment.
Experience with multi-modal AI systems (vision, voice, code generation).
Contributions to open-source agentic AI frameworks or published research in related areas.
Knowledge of AI safety, alignment research, and responsible AI practices.
Experience with observability and evaluation tooling for AI agents (e.g., LangSmith).
Background in domain-specific agent applications such as DevOps automation, customer support, data engineering, or security operations.