AI/ML Director

JPMorganChase; Greater London; On-site; GBP 120,000 - 150,000; Full time

Benefits offered by this job

Diversity and inclusion initiatives

Career development programs

Job summary

A leading global financial services firm seeks a Director of AI/ML to lead the AI portfolio, optimizing complex business workflows. You will guide cross-functional teams, design multi-agent systems, and implement advanced AI solutions. The ideal candidate will have an MSc/PhD in a relevant field, extensive AI application experience, and strong communication skills. This role offers a significant opportunity to impact business strategy through advanced technology deployment facilitated by a commitment to diversity and inclusion.

Qualifications

• Significant proven experience building and deploying AI applications.
• Experience managing data science teams and coaching team members.
• Strong understanding of data preprocessing and evaluation techniques.

Responsibilities

• Architect and develop autonomous AI agents.
• Implement Retrieval-Augmented Generation pipelines.
• Mentor and guide team members on agent frameworks.

Skills

Machine Learning frameworks

Python

Cloud platforms

Data analysis

Agent frameworks

Communication skills

Education

MSc/PhD in Computer Science or related field

Tools

PyTorch

TensorFlow

Docker

Kubernetes

Job description

Overview

Job Description — As a Director of AI/ML within the Commercial and Investment Bank, you will set the vision and lead the enterprise portfolio for agentic AI that automates and optimizes complex business workflows at scale. You will guide cross-functional teams and strategic partners to define reference architectures and delivery roadmaps for multi-agent systems—leveraging LLMs, retrieval-augmented generation, and modern agent frameworks—while instituting governance, safety, and reliability standards and tracking measurable business outcomes.

Responsibilities

• Architect, develop, and productionize autonomous and assistive AI agents to streamline and enhance operations.
• Design multi-agent systems, including role definition, tool integration, planning, memory, and workflow orchestration using LangChain, CrewAI, AutoGen, ADK and LangGraph.
• Implement Retrieval-Augmented Generation (RAG) pipelines and semantic search using vector databases such as Pinecone and Chroma, including indexing, retrieval policies, and evaluation.
• Build and integrate agent tools (MCP) and APIs to connect agents with external services, databases, and internal systems, ensuring robust output parsing, error handling, and retries.
• Design and implement robust evaluation frameworks to systematically assess and measure the performance of AI agents across key operational metrics.
• Practice advanced prompt and context engineering (e.g., Chain-of-Thought, ReAct, function calling/tool-use prompts), implement output validation and guardrails to reduce hallucinations.
• 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.
• Partner with stakeholders to define requirements, design intuitive human-AI interfaces (voice, chat), and deliver measurable business impact.
• Analyze data to inform agent capabilities, optimize retrieval, and drive data-driven decision-making; and performance evaluations.
• Mentor and guide team members on agent frameworks, LLM usage, safety, and best practices.

Required Qualifications

• MSc/PhD in Computer Science, Data Science, Machine Learning, or related field.
• Significant proven experience building and deploying AI applications in large scale production environments.
• Experience managing data science teams and coaching team members.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Hands-on experience with agentic frameworks (LangChain, CrewAI, AutoGen, LangGraph, ADK).
• Experience with generative models (transformers, GANs/VAEs; diffusion models a plus).
• Strong understanding of data preprocessing, feature engineering, and evaluation techniques.
• Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Strong communication skills for both technical and non-technical audiences.

Preferred Qualifications

• Experience fine-tuning small language models (SLMs) with LoRA, QLoRA, DoRA; quantization and distillation a plus.
• Familiarity with prompt optimization frameworks (AutoPrompt, DSPy) and building evaluation suites.
• Experience with distributed computing, data sharding, and performance optimization.
• Hands-on with AWS AI deployment services (SageMaker, Bedrock) and workflow orchestration.
• Demonstrated experience in financial services, particularly investment banking operations.

About Us

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.