MLOps Technical Manager
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
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 openly
- Empathetic team leader who drives accountability and high performance without micromanaging
Interested? Get in touch to find out more and have a confidential conversation about the role.