MLOps Technical Manager

As a Tech Lead with a strong MLOps engineering background, you will lead the design, architecture, and delivery of ML infrastructure, owning the end-to-end stack from backend to frontend while driving MLOps best practices across the team. You will work closely with data scientists, engineers, and cross-functional stakeholders to deliver scalable ML systems—including predictive maintenance, fault detection, and component lifecycle optimization—while mentoring and guiding your engineering team.

• 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 minimizing 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, alongside robust ETL/ELT pipelines.
• Develop and maintain React-based frontend interfaces that surface ML insights to operational and engineering stakeholders.
• Lead, mentor, and provide structured feedback to a team of 5+ engineers, fostering a high-performance culture.
• Collaborate with cross-functional stakeholders across engineering, data science, and operations while proactively addressing technical blockers.

• 10+ years of experience in Software, Data, or ML Engineering roles.
• Proven track record as a Tech Lead (managing teams 5+ people), owning end-to-end technical delivery across backend and frontend systems.
• Deep expertise in MLOps (model training pipelines, serving infrastructure, monitoring, CI/CD) and expert-level proficiency in Python.
• Strong hands-on experience with MLflow (mandatory) and 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 containerization (Docker) and scalable data systems (e.g., Spark, Kafka).
• Strong leadership presence, excellent communication, strategic thinking, and empathy with a hands-on execution mindset

Desirable Requirements:

• 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.
• 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.

Package:

• £100K – £120K + package
• The chance to join an organization with triple-digit growth that is changing the paradigm on how software products are built.
• The opportunity to be part of an amazing, multicultural community of tech experts.
• A highly competitive compensation package.
• A flexible and hybrid working environment.
• Medical insurance.