Senior MLOps Engineer

MUFG Investor Services
London
1 month ago
Create job alert

Company Description

MUFG Investor Services is a trusted partner to many of the world’s largest public and private funds, providing asset servicing and operational solutions built for alternatives. With over $1 trillion in client assets under administration, we offer fund administration, banking, payments, fund financing, foreign exchange overlay, corporate and regulatory services, custody, business consulting, and more. Operating from 17 locations worldwide, we help clients mitigate risk, enhance efficiency, and navigate the operational complexities of today’s investment management landscape. As a division of Mitsubishi UFJ Financial Group (MUFG), one of the world’s largest financial institutions with approximately $3 trillion in assets, we combine deep expertise with the strength and stability of a leading financial institution. To learn more, visit us at .


#LI-Hybrid

Job Description

We are seeking a highly skilled MLOps / Platform Engineer with a strong background in DevOps workflows and platform engineering best practices to join our AI initiative. This is a high-visibility project focused on deploying and managing AI agents across our infrastructure. You will work closely with the Research & Data Science team, backend and frontend engineers, and other technical teams to build a secure, scalable, and cost-optimized platform for AI workloads.

This position supports AI Engineering and Data Science initiatives by focusing on infrastructure, operations, and platform reliability. The Platform Engineer will work closely with AI Engineers and Data Scientists to ensure they have robust, scalable infrastructure to deploy their work.

You Will:

Design, deploy, and maintain AI agents on Agent Core MCP servers and MCP gateways. Implement and manage observability using OpenTelemetry for logs and traces, integrating with Datadog. Ensure security, high availability, and cost optimization across all AI platform components. Provide infrastructure and deployment support to AI researchers and engineering teams, enabling integration of cutting-edge technologies into production. Perform load testing, token cost measurement, and optimize resource utilization. Facilitate external vulnerability assessments and ensure compliance with security best practices. Troubleshoot and resolve platform issues promptly to maintain operational stability. Contribute to DevOps workflows, CI/CD pipelines, and automation for AI deployments. Support evaluation of third-party products related to hosting AI agents or enhancing project capabilities. Assist in external audits and maintain documentation for platform architecture and processes. Develop and execute automation scripts using the AWS Boto3 SDK to deploy, test, and validate AI platform components across multiple environments. Implement Infrastructure as Code (IaC) using Terraform to provision and manage cloud resources for AI workloads, ensuring consistency and scalability.

#LI-Hybrid

Qualifications

You Have:

5+ Years of experience in Platform Engineering / DevOps practice Deep understanding of DevOps principles, workflows, and best practices. Proven experience in platform engineering and full-stack development. Proficiency in API design and integration. Hands-on experience with AWS services Familiarity with OpenTelemetry, Datadog, and observability tooling. Solid coding skills in languages commonly used for backend and automation (, Python, , Go). Knowledge of microservices, container orchestration (Kubernetes/EKS), and cloud-native architectures. Extensive knowledge of security practices, cost optimization, and performance testing. Interest and familiarity with latest trends in MCPs (Model Context Protocol) and AI agent frameworks.

Preferred Experience

Working with AI/ML platforms or deploying AI agents in production environments. Exposure to high-scale distributed systems and cloud infrastructure. Experience in observability and monitoring for complex systems. AWS certifications

Project Details

High visibility within the organization. Opportunity to work with cutting-edge AI technologies and collaborate with leading experts.

Additional Information

What’s in it for you to join MUFG Investor Services? 

Take a look at our careers site and you’ll find everything you’d expect working with one of the fastest-growing businesses at one of the world’s largest financial groups. Now take another look. Because it’s how we defy expectations that really defines us. You’ll feel that difference in all kinds of ways. Our vibrant CULTURE. Connected team. Love of innovation, laser client focus. 

So, why settle for the ordinary? Apply now for your next Brilliantly Different opportunity. 

We thank all candidates for applying; however, only those proceeding to the interview stage will be contacted.

MUFG is an equal opportunity employer.

Related Jobs

View all jobs

Senior MLOps Engineer

Senior MLOps Engineer

Senior MLOps Engineer - Production ML at Scale

Senior MLOps Engineer - Scale & Automate ML Platforms

Senior MLOps Engineer — Build Production ML Pipelines

Senior MLOps Engineer: Scale AI Pipelines

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.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.