> Markdown version of [/jobs/ext/3196117-aiml-lead-platform-ai-acceleration](https://www.wearedevelopers.com/jobs/ext/3196117-aiml-lead-platform-ai-acceleration). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AIML Lead-Platform AI Acceleration - **Company:** JPMorgan Chase & Co. - **Location:** Glasgow, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Artificial Intelligence, Systems Engineering, Code Review, Nvidia CUDA, Software Debugging, Distributed Computing Environment, JSON, Python (Programming Language), Performance Tuning, Software Tools, Systems Architecture, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Web Filtering, Data Analytics, Natural Language Generation - **Published:** September 18, 2026 - **Apply:** https://www.themuse.com/jobs/jpmorganchase/applied-aiml-leadplatform-ai-acceleration ## About the Role * 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, * Financial Services industry experience * Understanding of Finops for LLMs * Good to have Java programming experience ## Description 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., * 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., Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success. ## Related Videos - [Create AI-Infused Java Apps with LangChain4j](https://www.wearedevelopers.com/videos/1549-create-ai-infused-java-apps-with-langchain4j) - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [Coffee with Developers - Stephen Jones - NVIDIA](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) - [Are Code Reviews Worth It? Insights from 16 Years of Review Data](https://www.wearedevelopers.com/videos/1135-are-code-reviews-worth-it-insights-from-16-years-of-review-data) - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Introducing JSON Structure](https://www.wearedevelopers.com/videos/100219-introducing-json-structure) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)