Applied AI ML Director at JPMC Candidate Experience page

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

19 May, 26

Salary

0.0

Posted On

18 Feb, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Java, Python, GenAI Platforms, VertexAI, AWS Bedrock, LangChain, Cloud Technologies, CI/CD Tools, Docker, Kubernetes, LLM-Model Applications, Vector Stores, Microservices Architecture, RAG, Agentic System Architectures, Agentic Frameworks

Industry

Financial Services

Description

Out of the successful launch of Chase in 2021, we’re a new team, with a new mission. We’re creating products that solve real world problems and put customers at the center - all in an environment that nurtures skills and helps you realize your potential. Our team is key to our success. We’re people-first. We value collaboration, curiosity and commitment. As a Applied AI ML Director at JPMorganChase within the Accelerator Business, you are the heart of this venture, focused on getting smart ideas into the hands of our customers. You have a curious mindset, thrive in collaborative squads, and are passionate about new technology. By your nature, you are also solution-oriented, commercially savvy and have a head for fintech. You thrive in working in tribes and squads that focus on specific products and projects – and depending on your strengths and interests, you'll have the opportunity to move between them. While we’re looking for professional skills, culture is just as important to us. We understand that everyone's unique – and that diversity of thought, experience and background is what makes a good team, great. By bringing people with different points of view together, we can represent everyone and truly reflect the communities we serve. This way, there's scope for you to make a huge difference – on us as a company, and on our clients and business partners around the world. Job Responsibilities Design and develop scalable, self-service solutions for documentation, SDKs, configurations and pipelines to enable rapid deployment of GenAI applications (including Retrieval-Augmented Generation (RAG) pipelines) and agents with planning, memory, and workflow orchestration Implement tools and frameworks for model versioning, experiment tracking, and lifecycle management Develop systems to monitor model performance and address data and model drift Recommend best practices for model integration and deployment patterns Design and implement effective testing strategies, including unit, component, integration, end-to-end, performance, and champion/challenger tests, establish output validation best practices, recommendations and guardrails to reduce hallucinations. Ensure platform compliance with data privacy, security, and regulatory standards Mentor team members on platform design principles and best practices Guide colleagues on coding practices, design principles, and implementation patterns for high-quality, maintainable solutions Deploy scalable AI services to cloud infrastructure, ensuring monitoring, and observability for agent performance. Design microservices-based architectures and orchestrate multi-step workflows; instrument agents for tracing, metrics, and feedback loops to continuously improve reliability and utility. Required qualifications, capabilities and skills: Demonstrate proficiency in Java and/or Python programming languages Deployed production systems to GenAI platforms such as Google VertexAI, OpenAI, AWS Bedrock, or LangChain Utilized cloud technologies (AWS/Azure/GCP), distributed systems, CI/CD tools, infrastructure-as-code tools, and containerization/orchestration tools (Docker, Kubernetes) to operate, support, and secure mission-critical applications Previous experience deploying and managing LLM-model based applications and agents Exposure to vector stores such as Pinecone, GCP RAG engine, and AWS S3 Vector Buckets Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols, including Retrieval-Augmented Generation (RAG), agentic system architectures, and Model Context Protocol (MCP) Hands-on experience with agentic frameworks (LangChain, CrewAI, AutoGen, LangGraph, ADK). Strong communication skills for both technical and non-technical audiences. Preferred qualifications, capabilities and skills: Experience working in highly regulated environments or industries Experience with distributed computing, data sharding, and performance optimization. Demonstrated experience in financial services, particularly retail banking operations. J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. 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.

Responsibilities

The Director will design and develop scalable, self-service solutions for deploying GenAI applications, including RAG pipelines, agents with planning, memory, and workflow orchestration. Responsibilities also include implementing tools for model versioning, monitoring performance, establishing testing strategies, and ensuring platform compliance with security and privacy standards.