Senior Applied AI/ML Lead — GenAI & RAG Architect

JPMorganChase; Glasgow; On-site; GBP 120,000 - 180,000; Full time

Job summary

J.P. Morgan is seeking an Applied ML and Generative Lead within its Infrastructure Platforms team. The role combines hands-on engineering leadership with the design, build, and operation of production-grade ML and GenAI services at scale.

You will mentor engineers, set engineering standards, and ensure solutions meet enterprise security, stability, and operational requirements while collaborating with AI teams across the firm.

Qualifications

• Proven delivery of LLM-enabled applications using agentic patterns.
• Hands-on experience building and operating MCP integrations reliably in production.
• Hands-on experience on data-driven software/systems engineering 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)

Responsibilities

• Lead design, development, and deployment of ML/LLM/GenAI solutions from concept to production.
• Collaborate with product managers, data scientists, ML engineers, and stakeholders to understand requirements.
• Develop secure, testable services and libraries integrating LLMs, tools, RAG, and agentic workflows.
• Build end-to-end RAG/Agentic RAG pipelines: chunking, indexing, retrieval tuning, re-ranking, grounding checks.
• Implement optimization strategies to fine-tune generative models for NLP use cases with high-quality outputs.
• Mentor junior engineers through design reviews, code reviews, pairing, and coaching.
• Implement monitoring mechanisms to track AI solution performance in real-time for reliability.
• Communicate AI/ML/LLM/GenAI capabilities and results to technical and non-technical audiences.
• Stay informed about the latest trends in AI/ML/GenAI, implement cutting-edge techniques, and leverage external APIs.

Skills

LLM-enabled apps

Agentic AI systems

Python engineering

Production-grade design

Prompt engineering

Distributed training/serving

CUDAGPU acceleration

RAG/Graph RAG

Security & Responsible AI

Tool integration & guardrails

Mentoring engineers

Communication with stakeholders

Tools

Ray

CUDA

Job description

J.P. Morgan is seeking an Applied ML and Generative Lead within its Infrastructure Platforms team. The role combines hands-on engineering leadership with the design, build, and operation of production-grade ML and GenAI services at scale.

You will mentor engineers, set engineering standards, and ensure solutions meet enterprise security, stability, and operational requirements while collaborating with AI teams across the firm.