MLOps manager

Uniting Ambition; Slough; On-site; GBP 100,000 - 150,000; Full time

Job summary

Uniting Ambition seeks an MLOps Technical Manager to lead a growing ML engineering function and deliver scalable ML systems across backend and frontend. You'll work with data scientists and engineers, guiding architecture, pipelines, and deployment practices.

The role combines hands-on technical work (about 40%) with people management and senior stakeholder engagement, requiring strong leadership and communication skills.

Qualifications

• 10+ years in Software, Data, or ML Engineering roles.
• Proven track record as a Tech Lead (5+ people).
• Deep expertise in MLOps including training, serving, monitoring, and CI/CD.
• Strong Python proficiency (essential).
• Hands-on MLflow experience is mandatory.
• Experience with AWS and cloud-native architectures.
• Frontend experience with React for end-to-end features.

Responsibilities

• Own end-to-end delivery of ML systems from backend to frontend.
• Lead architectural decisions for scalable ML stack.
• Drive migration from MLflow to AWS SageMaker with minimal disruption.
• Define and enforce MLOps best practices across pipelines.

Skills

MLOps

Python

AWS

React

Docker

Spark

Kafka

Leadership

CI/CD

Stakeholder communication

Tools

MLflow

SageMaker

Terraform

Job description

MLOps Technical Manager

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

MLOps & Engineering

• 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 openlyEmpathetic team leader who drives accountability and high performance without micromanaging