MLOps manager

Uniting Ambition; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

Uniting Ambition is seeking an MLOps Technical Manager in West London to lead a growing ML engineering function. The role combines hands-on technical ownership (40%) with people management and senior stakeholder engagement, guiding ML systems from backend to frontend.

You will collaborate with data scientists, engineers, and operations to deliver scalable ML capabilities, including predictive maintenance and real-time data processing, while promoting CI/CD and engineering excellence.

Qualifications

• 10+ years of experience in Software, Data, or ML Engineering roles.
• Tech Lead experience with 5+ people and end-to-end delivery across backend and frontend.
• Deep expertise in MLOps: training pipelines, serving, monitoring, CI/CD.

Responsibilities

• Own end-to-end technical delivery of ML systems from backend to frontend.
• Lead architectural decisions across the ML stack for scalability and reliability.
• Drive migration from MLflow to AWS SageMaker with minimal disruption.
• Define and enforce MLOps best practices for training, serving, monitoring, and deployment.
• Design scalable ML infrastructure for batch and real-time processing.
• Oversee ETL/ELT pipelines, feature engineering, and data processing at scale.
• Develop React-based frontend interfaces to surface ML insights to stakeholders.
• Champion CI/CD practices and engineering excellence.

Skills

Python

MLOps

React

CI/CD

Leadership

Team management

AWS

Tools

Docker

Spark

Kafka

AWS

Job description

West London, UK (Hybrid – 2-3 days on-site)| Permanent

About the Role

We're recruiting on behalf of a major, well-established organisation for an MLOps Technical Manager to lead a growing ML engineering function. This is a genuinely interesting project: you'll work closely with data scientists, maintenance engineers, and cross-functional stakeholders to deliver scalable ML systems — including predictive maintenance, fault detection, and component lifecycle optimisation.

You'll own end-to-end technical delivery of ML systems, from backend infrastructure through to frontend integration, while leading and mentoring a team of engineers. This is a hands-on leadership(40% hands on) role for someone who wants to combine deep technical ownership with people management and senior stakeholder engagement.

Key Responsibilities

Technical Leadership

• Own the end-to-end technical delivery of ML systems, from backend infrastructure to frontend integration
• Lead architectural decisions across the ML stack, ensuring scalability, reliability, and alignment with business goals
• Drive the ongoing migration from MLflow to AWS SageMaker, maintaining continuity and minimising disruption
• Define and enforce MLOps best practices across model training, serving, monitoring, and deployment
• Design and maintain scalable ML infrastructure supporting batch and real-time environments
• Oversee the implementation of robust ETL/ELT pipelines, feature engineering, and data processing at scale
• Ensure model training, serving, and monitoring pipelines are production-grade and optimised for performance
• Develop and maintain React-based frontend interfaces that surface ML insights to operational and engineering stakeholders
• Champion CI/CD practices and contribute to a culture of engineering excellence

Team & Stakeholder Management

• Lead, mentor, and provide structured feedback to a team of engineers, fostering a high-performance culture
• Collaborate closely with cross-functional stakeholders across engineering, data science, and operations
• Communicate complex technical decisions clearly to both technical and non-technical audiences
• Identify and address technical blockers proactively, keeping delivery momentum and team

Qualifications

Must-Have

• 10+ years of experience in Software, Data, or ML Engineering roles
• Proven track record as a Tech Lead (managing teams of 5+ people), owning end-to-end technical delivery across backend and frontend systems
• Deep expertise in MLOps, including model training pipelines, serving infrastructure, monitoring, and CI/CD
• Expert-level proficiency in Python — this is non-negotiable
• Strong hands-on experience with MLflow (mandatory)
• Solid experience with AWS and cloud-native architectures
• Frontend proficiency in React, with the ability to deliver end-to-end product features
• Hands-on experience with ETL/ELT pipelines, data engineering, and large-scale data processing
• Experience with containerisation (Docker) and scalable data systems (e.g. Spark, Kafka)

Nice-to-Have

• Experience with AWS SageMaker or similar managed ML platforms
• Background in safety-critical or regulated industries (aerospace, aviation, or similar)
• Familiarity with Kafka or event-driven architectures for real-time ML pipelines

Soft Skills

• Strong leadership presence with the ability to give and receive structured, constructive feedback
• Excellent communicator across technical and business stakeholders — comfortable navigating complex organisational dynamics
• Strategic thinker with a hands-on execution mindset
• Proactive and collaborative, with the confidence to ask questions, challenge assumptions, and share ideas openly
• Empathetic team leader who drives accountability and high performance without micromanaging