Applied AIML Lead-Platform AI Acceleration

Join a specialist team as Lead AI/ML Engineer building reusable AI/ML components to accelerate product delivery.

The Applied Artificial Intelligence and Machine Learning (Applied AI/ML) team within Infrastructure Platforms is transforming how the firm delivers strategic infrastructure platforms-based solutions—both by applying AI/ML within engineering workflows and by building scalable AI hosting platforms and capabilities for enterprise use. As an Applied ML and Generative Lead within J.P.Morgan, you will operate as a hands-on engineering leader responsible for designing, building, and running production-grade ML and Generative AI services, while setting technical direction that scales across multiple workstreams. You will remain close to the code and architecture decisions, establish delivery and engineering standards, and ensure solutions meet enterprise expectations for security, stability, and operational rigor. The ideal candidate brings a strong foundation in software engineering and AI/ML, along with proven experience leading the development and production operation of AI-enabled systems in secure, enterprise environments. In this role, you will collaborate closely with Infrastructure Platforms AI teams to address priority use cases, design and build services, and promote best practices for scalable, resilient, and secure AI adoption. You will also mentor engineers, contribute to firmwide standards and thought leadership, and help ensure the organization stays at the forefront of AI engineering advancements. Job Responsibilities Provide hands-on technical leadership by designing, developing, and deploying ML/LLM/GenAI solutions from concept through production, maintaining ownership for reliability and operability once deployed Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases. Develop secure, testable services and libraries that integrate LLMs, tool use, RAG, and agentic workflows. Build end-to-end RAG/Agentic RAG pipelines: chunking and indexing, retrieval tuning, re-ranking, grounding checks. Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation. Mentor and uplift junior engineers through design reviews, code reviews, pairing, and coaching, raising engineering quality and delivery discipline across the team. Implement monitoring mechanisms to track AI solution performance in real-time to ensure reliability and compliance. Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences. Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality. Required qualifications, capabilities, and skills Proven delivery of LLM-enabled applications using agentic patterns, including tool use, orchestration, guardrails, and structured outputs. Hands-on experience building and operating MCP integrations reliably in production. Hands-on experience on data-driven software/systems engineering experience delivering production services in secure, regulated environments. Expertise in Python engineering skills, including production-grade design, testing, debugging, and performance tuning/optimization. Advanced prompt engineering capabilities, including system prompts, few-shot prompting, tool/function calling, and schema-constrained outputs (e.g., JSON Schema). Understanding of agentic AI system layers and concepts, such as context management, harness design, and loop engineering. Experience building conversational AI solutions, including RAG, Agentic and Graph RAG techniques Experience building and scaling AI/ML workloads using distributed training/serving frameworks (e.g., Ray) and GPU acceleration (e.g., CUDA) environments. Proficiency with modern AI system architectures and patterns, including RAG, agentic RAG, and multi-agent systems. Familiarity with LLM evaluation methodologies across quality, safety, and reliability, including guardrails, content filtering, and Responsible AI practices. Proficiency in GenAI/agentic AI engineering practices, including data sensitivity, secure handling of inputs/outputs, and adherence to resiliency and security requirements Demonstrated success driving adoption of enterprise-approved AI-assisted engineering tools (coding, review, testing, troubleshooting Preferred qualifications, capabilities, and skills Financial Services industry experience Understanding of Finops for LLMs Good to have Java programming experience