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
We use essential cookies to make this site work. With your permission we would also like to use the optional cookies below. Nothing optional is set until you choose, and you can change your mind at any time from "Cookie settings" at the bottom of every page. Cookie policy.
Essential — keep the site working and secure, and remember the choice you make here. Always on.