Software Engineer Python - Advanced | Columbus, US | JPMC

Role Overview

We are looking for an Advanced Python Engineer / Agentic AI FDE with strong hands-on experience in Python development and a solid understanding of Generative AI, LLMs, AI Agents, and agentic application development.

The ideal candidate will work closely with client engineering and product teams to design, develop, integrate, and deploy production-grade agentic AI solutions. This role requires strong software engineering fundamentals, the ability to work with modern AI frameworks, and excellent client-facing problem-solving skills.

Key Responsibilities

• Design and develop scalable Agentic AI applications and AI-powered solutions using Python.
• Build, integrate, and optimize AI agents, multi-agent workflows, tools, and orchestration pipelines.
• Develop production-grade Python services, APIs, integrations, and backend components.
• Work with LLMs, prompt engineering, RAG, embeddings, vector databases, and tool/function calling.
• Implement agent workflows using frameworks such as LangChain, LangGraph, OpenAI Agent SDK, Google ADK, CrewAI, AutoGen, or similar frameworks.
• Integrate AI agents with enterprise systems, APIs, databases, SaaS platforms, and business applications.
• Develop and consume REST APIs, microservices, and event-driven integrations.
• Implement appropriate mechanisms for agent memory, context management, state management, and knowledge retrieval.
• Work with cloud-based AI platforms such as AWS Bedrock, Azure AI Foundry, or Google Cloud Vertex AI/Gemini.
• Implement observability, monitoring, logging, evaluation, and performance optimization for AI applications.
• Collaborate with architects, product managers, data scientists, and client stakeholders to translate business requirements into technical solutions.
• Participate in client discussions, technical workshops, solution demonstrations, and proof-of-concepts.
• Troubleshoot complex technical issues and provide hands-on engineering support during implementation.
• Follow secure and responsible AI engineering practices, including appropriate authentication, authorization, data protection, and AI governance.

Required Technical Skills

Python – Advanced

• Strong hands-on expertise in Python.
• Advanced knowledge of Python programming concepts, OOP, data structures, exception handling, concurrency/asynchronous programming, and performance optimization.
• Experience building production-grade applications and APIs using frameworks such as FastAPI, Flask, or Django.
• Strong understanding of testing, debugging, logging, packaging, and dependency management.

Agentic AI / Generative AI

• Strong understanding of LLMs, Generative AI, AI Agents, Agentic AI, and LLM application architecture.
• Hands-on experience with one or more agent frameworks such as:
  • LangChain / LangGraph
  • OpenAI Agent SDK
  • Google ADK
  • CrewAI
  • AutoGen
  • Microsoft Agent Framework or equivalent
• Experience with RAG, vector search, embeddings, prompt engineering, tool/function calling, structured outputs, and agent orchestration.
• Understanding of agent memory, context management, multi-agent systems, and agent evaluation.

Cloud & AI Platforms

• Experience with at least one major cloud platform: AWS, Azure, or GCP.
• Exposure to AI platforms such as Amazon Bedrock, Azure AI Foundry, Vertex AI/Gemini, or equivalent.
• Understanding of deploying AI applications in cloud environments.

APIs & Integration

• Strong experience with REST APIs, JSON, web services, authentication, and third-party integrations.
• Experience integrating AI solutions with enterprise applications and data sources.
• Understanding of microservices and distributed application architecture.

Data & Databases

• Experience with SQL and relational databases.
• Exposure to NoSQL databases and vector databases such as Pinecone, Weaviate, Milvus, pgvector, or equivalent.
• Understanding of data ingestion, retrieval, chunking, embeddings, and knowledge bases.

Preferred Skills

• Experience with Docker, Kubernetes, CI/CD, Git, and cloud deployment.
• Exposure to AI observability and evaluation tools.
• Understanding of AI governance, guardrails, responsible AI, and secu