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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
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Lead Software Engineer and ML Engineer - Sales Copilot serves as a core technical contributor within the Digital & Platform Services, Data Analytics team. This role involves designing, productionizing, and operating LLM-powered agents on the NEO platform to solve complex problems across Commercial & Investment Bank and Payments divisions. Key responsibilities include building retrieval systems using Graph RAG, designing agent memory with episodic and semantic nodes, managing organizational context, and composing multi-agent workflows via MCP servers. The candidate will utilize Python, SQL, NoSQL stores like OpenSearch or Redis, and infrastructure tools including Kubernetes, Amazon EKS, AWS, and Azure. Essential skills include experience with vector stores, LLM evaluation suites, and security practices like mTLS and runtime isolation. The role requires expertise in information retrieval, data mining, and the financial services industry to deliver stable, scalable technology products.
Design and ship production LLM-powered agents on the NEO platform from prototype through deployment.
Build robust retrieval systems using Graph RAG, vector search, and advanced ranking strategies.
Design agent memory architectures including episodic and semantic memory with custom recall policies.
Manage organizational context to ensure agents only access authorized data based on specific entitlements.
Compose multi-agent workflows and integrate tools via MCP servers for platforms like Databricks and Snowflake.
Develop comprehensive evaluation suites, including LLM-as-judge and safety gating, before product release.
Deploy and operate scalable solutions on AWS or Azure using Kubernetes and secure SDLC practices.
Lead technical evaluations with external vendors and internal teams to validate architectural designs.
What we're looking for
Formal training or certification on software engineering concepts and 5+ years of experience.
At least 4 years of experience working on AI/ML solutions.
Advanced programming skills in Python with deep knowledge of data structures, algorithms, machine learning, and information retrieval.
Hands-on experience building LLM-powered or agentic applications in production including tracing, evaluations, and guardrails.
Proficiency with Kubernetes, Amazon EKS, micro-VM isolation, and designing defense-in-depth security architectures.
Practical RAG experience with vector stores and expert knowledge of AWS, Azure, or Kubernetes.
Knowledge of data management, SQL (e.g., Postgres), and NoSQL stores (e.g., OpenSearch, Redis).
MS in Computer Science, Statistics, Mathematics, Machine Learning, or related field (preferred); Experience with Graph RAG, Go/Rust, or full-stack JS/TS frameworks (preferred).
Python
LLMs
RAG
Kubernetes
Docker
Terraform
Micro-VMs
CI/CD
Machine Learning
Vector Databases
Agentic Systems
Infrastructure as Code
Agile
Software Development Life Cycle
Data Observability
Graph RAG
Firecracker
Kata
gVisor