Sr Lead Software Engineer - Agentic AI Systems

JPMorgan Chase JPMorgan Chase · Banking · Wilmington, DE +1 · Consumer & Community Banking

Senior Lead Software Engineer focused on architecting, building, and scaling autonomous AI agent systems. Requires deep experience in agentic AI, LLMs, Python frameworks (LangChain, etc.), cloud platforms, and vector databases. The role involves technical leadership and driving the delivery of production-grade AI agent systems.

What you'd actually do

  1. 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.
  2. 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.
  3. You will lead and influence engineers across the organization and collaborate cross-functionally to deliver production-grade AI agent systems and infrastructure.
  4. Develops secure and high-quality production code, and reviews and debugs code written by others
  5. Drives decisions that influence the product design, application functionality, and technical operations and processes

Skills

Required

  • 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

Nice to have

  • 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.

What the JD emphasized

  • 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.
  • Proficiency with vector databases (Pinecone, Weaviate, Qdrant, pgvector), embedding models, and RAG architectures.

Other signals

  • architect, build, and scale autonomous AI agent systems
  • production-grade AI agent systems and infrastructure
  • technical lead, staff, or principal-level individual contributor role
  • 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
  • Proficiency with vector databases (Pinecone, Weaviate, Qdrant, pgvector), embedding models, and RAG architectures