Senior Lead Software Engineer, Python AI

JPMorgan Chase

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Quick summary

Work type: On-site
Location: Plano, TXColumbus, OHJersey City, NJWilmington, DE
Employment: Full-time
Posted: 7 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $175k

$107k $238k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $149,500–$200,262.

Based on 240 similar postings.

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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.

JPMorgan Chase currently has 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · Senior Lead Software Engineer, Python AI

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The Senior Lead Software Engineer- Python AI will join the engineering team to enhance multiple products by identifying and delivering AI-driven features. The role involves designing, building, and deploying production-grade AI applications, including LLM-powered workflows, Retrieval-Augmented Generation solutions, and agentic systems with enterprise-ready controls. Key responsibilities include developing automation solutions, establishing AI engineering standards, and conducting architecture reviews for model hosting and data access. The candidate must possess 5+ years of applied engineering experience and proficiency in Python, Django, FastAPI, and React. Technical requirements include experience with AWS, containerized deployments, and observability tools. The role addresses complex problems regarding latency, scalability, cost management, and model risk, while utilizing frameworks like LangChain, LlamaIndex, and vector databases such as Pinecone or Weaviate.

Skills

Python LLM RAG LangChain LlamaIndex FastAPI Django React AWS Terraform Pinecone pgvector OpenSearch Databricks CI/CD System Design Observability Vector Search

What you'll do

Design, build, and deploy production-grade AI applications including LLM-powered workflows, RAG solutions, and agentic systems.
Build agentic workflows with enterprise-ready controls for identity, authorization, guardrails, tracing, and auditability.
Architect AI systems focusing on latency, scalability, cost management, observability, and reliability.
Develop automation solutions to reduce manual effort and eliminate recurring operational issues through self-healing patterns.
Establish AI engineering standards for prompting, RAG design, evaluation practices, and release readiness across the product line.
Identify and mitigate risks including data privacy, security, bias, prompt injection, and third-party vendor risks.
Lead technical evaluations with vendors to assess capabilities, integration fit, security posture, and total cost of ownership.
Write secure, high-quality production code and perform technical troubleshooting and code reviews.

What we're looking for

Formal training or certification in software engineering concepts and 5+ years of applied engineering experience.
Hands-on experience delivering AI-powered features in production or near-production environments including LLMs, RAG, and agents.
Strong proficiency in Python to build maintainable services, libraries, and pipelines.
Experience building APIs and services using Django, FastAPI, or similar frameworks.
Experience building modern UIs using React or similar frameworks (or strong ability to partner closely with frontend teams).
Strong system design skills with an emphasis on secure distributed systems, reliability, and operational excellence.
Practical understanding of Responsible AI concepts including evaluation, guardrails, privacy, bias, and safe deployment practices.
Experience with cloud platforms (AWS preferred) and containerized deployments.
Experience with AI orchestration frameworks like LangChain, LlamaIndex, or CrewAI (preferred).
Experience with vector search and retrieval patterns including pgvector, Pinecone, or Weaviate (preferred).
Experience designing and operating RAG systems including chunking strategies and retrieval evaluation (preferred).
Strong Infrastructure-as-Code experience with Terraform and CI/CD practices (preferred).
Experience with data engineering ecosystems like Databricks (preferred).

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