Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Principal MLOps Engineer (15h Left)

JPMorgan Chase & Co.
London
7 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist: AI, MLOps & Production Leader

Principal Machine Learning Engineer

Senior Practice Lead - Data Science

Principal Data Scientist

Machine Learning Engineer (Databricks)

Data Engineer – GCP/DSS

We know that people want great value combined with anexcellent experience from a bank they can trust, so we launched ourdigital bank, Chase UK, to revolutionise mobile banking withseamless journeys that our customers love. We're already trusted bymillions in the US and we're quickly catching up in the UK – buthow we do things here is a little different. We're building thebank of the future from scratch, channelling our start-up mentalityevery step of the way – meaning you'll have the opportunity to makea real impact. As a Principal MLOps Engineer at JPMorgan Chasewithin the International Consumer Bank, you provide deepengineering expertise and work across agile teams to enhance,build, and deliver trusted market-leading technology products in asecure, stable, and scalable way. You are expected to be involvedin the design and architecture of the solutions while also focusingon the entire SDLC lifecycle stages. Our Machine LearningOperations team is at the heart of this venture, focused on gettingsmart ideas into the hands of our customers. We're looking forpeople who have a curious mindset, thrive in collaborative squads,and are passionate about new technology. By their nature, ourpeople are also solution-oriented, commercially savvy and have ahead for fintech. We work in tribes and squads that focus onspecific products and projects – and depending on your strengthsand interests, you'll have the opportunity to move between them.Job responsibilities: - Advise and lead development of tooling forAI/ML development and deployment. - Lead deployment and maintenanceof infrastructure, model monitoring and observability tools,providing an effective model development platform for datascientists and ML engineers. - Collaborate with machine learningmodel developers to bring ML models to production. - Mentor andlead a team of engineers focused on deploying machine learningpipelines at scale. - Partner with product, architecture, and otherengineering teams to define scalable and performant technicalsolutions. - Influence across business, product, and technologyteams and successfully manage senior stakeholder relationships. -Champion the firm’s culture of diversity, equity, inclusion, andrespect. Required qualifications, capabilities and skills - Formaltraining or certification on software engineering concepts andMLOps applied experience. - Experience with machine learningengineering and operations in a large enterprise. - Experience inbuilding, evaluating and deploying ML models into production. -Experience leading complex projects supporting system design,testing and operational stability. - Demonstrated prior experienceinfluencing across complex organizations and delivering value atscale. - Extensive practical cloud native experience. - Provenexpertise on adoption of agile practices to deliver efficiently andto the expected quality solutions. #ICBCareer #ICBEngineering#J-18808-Ljbffr

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.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.