Lead Software Engineer - Agentic AI/Machine Learning

JPMorganChase
Glasgow
1 month ago
Create job alert

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.


As a Lead Machine Learning Engineer, Agentic AI within Risk Technology at JPMorgan Chase, you will lead a specialized technical area, driving impact across teams, technologies, and projects. In this role, you will leverage your deep knowledge of software engineering, multi‑agent system design and leadership to spearhead the delivery of complex and groundbreaking initiatives that will transform Asset and Wealth Management Risk.


You will be responsible for hands‑on development, and leading and mentoring of a team of Machine Learning and Software Engineers, focusing on best practices in ML engineering, with the goal of elevating team performance to produce high‑quality, scalable systems. You will also engage and partner with data science, product and business teams to deliver end‑to‑end solutions that will drive value for the Risk business.


Responsibilities:

  • Lead the deployment and scaling of advanced generative AI, Agentic AI and classical ML solutions for the Risk Business.
  • Lead design and execution of enterprise‑wide reusable AI/ML frameworks and core infrastructure capabilities that will accelerate development of AI solutions.
  • Develop multi‑agent systems that provide capabilities for orchestration, agent‑to‑agent communication, memory, telemetry, guardrails, etc.
  • Conduct and guide research on context and prompt engineering techniques to improve the performance of prompt‑based models, exploring and utilizing Agentic AI libraries like JPMC's SmartSDK and LangGraph.
  • Develop and maintain tools and frameworks for prompt‑based agent evaluation, monitoring and optimization to ensure high reliability at enterprise scale.
  • Build and maintain data pipelines and data processing workflows for scalable and efficient consumption of data.
  • Develop secure, high‑quality production code, and provide code reviews.
  • Foster productive partnership with Data Science, Product and Business teams to identify requirements and develop solutions to meet business needs.
  • Communicate effectively with both technical and non‑technical stakeholders, including senior leadership.
  • Provide technical leadership, mentorship and guidance to junior engineers, promoting a culture of excellence, continuous learning, and professional growth.

Required qualifications, capabilities and skills:

  • Bachelor's degree or Master's in Computer Science, Engineering, Data Science, or related field
  • Applied experience in Machine Learning Engineering.
  • Strong proficiency in Python and experience deploying end‑to‑end pipelines on AWS.
  • Hands‑on practical experience delivering system design, application development, testing, and operational stability
  • Hands‑on experience using LangGraph or JPMC's SmartSDK for multi‑agent orchestration.
  • Experience with AWS and Infrastructure‑as‑code tools like Terraform.

Preferred Qualifications:

  • Strategic thinker with the ability to drive technical vision for business impact.
  • Demonstrated leadership working effectively with engineers, data scientists, and ML practitioners.
  • Familiarity with MLOps practices, including CI/CD for ML, model monitoring, automated deployment, and ML pipelines.
  • Experience with Agentic telemetry and evaluation services.
  • Demonstrated hands‑on experience building and maintaining user interfaces

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.


About the Team

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.


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Lead Software Engineer- Data Engineer, Java/Python

Senior Lead Software Engineer- Data Engineer, Java/Python

Senior Lead Software & Data Engineer - Java/Python

Senior Lead Data Engineer (Java/Python) - Cloud & AI

Senior Machine Learning Engineer

Lead AI Engineer — Production ML & MLOps Leader

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.