Software Engineer III - Python LLM Engineer

JOB DESCRIPTION

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Software Engineer III at JPMorganChase within the Asset and Wealth Management Technology team, 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.

Job Responsibilities

• Design and build agentic AI workflows capable of multi-step reasoning, tool orchestration, and task execution, with clear boundaries, fallback behavior, and human approvals where needed.
• Develop and productionize LLM-powered applications such as conversational interfaces, intelligent search, summarization/extraction services, and advisory assistants.
• Implement patterns for tool integration (APIs, internal services, data sources) so agents can safely retrieve data and take actions with authentication/authorization, audit logging, and least-privilege access.
• Build and maintain RAG components where applicable (ingestion, indexing, retrieval, grounding/citations, reranking) and tune for answer quality and latency.
• Produce architecture/design artifacts for distributed systems, including service boundaries, data flows, scalability, resiliency, and non-functional requirements (latency/throughput/availability).
• Establish evaluation and monitoring: offline test sets, automated regression checks, prompt/version management, hallucination/grounding checks, and runtime observability (traces, latency, cost, tool-call success rates).
• Apply practical data engineering / analytics skills to support AI features: data cleaning, normalization, deduplication, exploratory analysis, and basic KPI reporting on system quality and user behavior.
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
• Collaborate with product, design, and stakeholders to translate requirements into technical solutions, delivery plans, and iterative releases and ensure solutions meet security, privacy, and responsible AI expectations (safe handling of sensitive data, compliance-aware design, and controlled model outputs).
• Mentor engineers and contribute to shared standards, reusable components, and engineering best practices across AI delivery.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3+ years applied experience
• Experience in software engineering, including delivering production applications and services.
• Strong proficiency in Python and experience with APIs/microservices.
• Hands-on experience building LLM/RAG/agentic applications (prompting, structured outputs, tool/function calling, state management, error handling).
• Familiarity with agent frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.
• Working knowledge of data handling basics: data cleaning, joins/aggregations, simple statistics, exploratory analysis; comfortable with SQL and/or pandas-style workflows.
• Experience with cloud platforms (AWS or Azure), CI/CD, observability, and operational reliability practices.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectati…