Senior Staff MLOps Engineer

Boehringer Ingelheim GmbH; Greater London; On-site; GBP 110,000 - 150,000; Full time

Benefits offered by this job

Top Employer UK

Job summary

Boehringer Ingelheim in London seeks a senior leader in MLOps Engineering to own the architecture and standards for the AI Accelerator. You will drive the end-to-end model lifecycle from training to production deployment and governance.

You will define tooling, platforms and practices that enable large-scale training, experiment tracking, deployment, monitoring and governance, while coaching the growing team. This is a hybrid role with about 4 days in the office.

Qualifications

• PhD or MSc in STEM with equivalent experience.
• Extensive senior-level MLOps/ML Platform Engineering experience with strategy/architecture skills.
• Deep expertise across the MLOps lifecycle: training orchestration, experiment tracking, CI/CD for ML, deployment and monitoring.

Responsibilities

• Own the MLOps architecture and roadmap for the AI Accelerator, defining end-to-end model lifecycle including training orchestration, experiment tracking, model registries, CI/CD, deployment and monitoring.
• Establish MLOps standards and engineering practices including model testing/validation, containerisation, deployment patterns and release management.
• Govern model artefacts: weights, parameters, hyperparameters, model cards with versioning and lineage.

Skills

MLOps

ML Platform Engineering

Python

Docker

Kubernetes

CI/CD for ML

Experiment tracking

Model registries

Team leadership

Cloud platforms

Education

PhD or MSc in STEM

Tools

Docker

Kubernetes

Helm

Job description

Most diseases are still poorly understood at a biological level. Despite decades of research, the causal mechanisms driving many conditions remain unclear, limiting our ability to identify the right targets, design the right interventions and bring the right medicines to patients.

The AI Accelerator exists to change that.Based in London and sitting within Computational Innovation (@computationalinnovation), a global organisation spanning computational biology, human genetics, data excellence and AI, the Accelerator's mission is to build production-quality AI capabilities that deepen our understanding of disease biology and increase probability of success.

We do this by applying neural-based methods across the biomedical data landscape to integrate heterogeneous, multimodal data sources, infer biological relationships and embed causal thinking into what we build. The goal is not just to predict but to explain and understand why disease occurs.

It could be electronic health records and medical imaging to support patient segmentation. It could be 'omics data to identify novel therapeutic targets. It could be predicting transcriptional change for a given disease-causing variant. It could be simulating the effect of modulating a target of interest.

Breakthrough AI research only creates value when it can be reproduced, scaled, deployed and trusted.MLOpssits at the heart of that challenge. The AI Accelerator's ability to train biomedical foundation models, manage experimentation at scale, operationalise discoveries and deliver production-quality AI depends on the ML platform, tooling and practices that underpin the entire model lifecycle.

THE POSITION

We are seeking a senior leader inMLOpsEngineeringto join Computational Innovation's AI Accelerator (@computationalinnovation). In this role, you will own the MLOps architecture, standards and technical direction for the AI Accelerator, ensuring that models can move efficiently from experimentation to reliable production deploymentand support. You will define the tooling, platforms and engineering practices that enable large-scale model training, experiment tracking, deployment, monitoring and governance.

This is both a strategic and hands-on technical leadership role. You will define architecture and standards,helpsolve the most challenging ML platform problems yourself and serve as the senior technical authority forMLOpswithin the AI Accelerator.As a senior member of the AI Enablement leadership team, you will help shape the direction of the function alongside the Senior Staff Data Engineer and Senior Staff AI Infrastructure Engineer.

This is a unique opportunity to be part of a critical strategic initiative for a pharmaceutical company that invests heavily in research and development to discover and develop innovative therapies that can improve and extend lives in areas of high unmet medical need.

Key Responsibilities

• Own theMLOpsarchitecture and roadmap for the AI Accelerator, defining and evolving the end-to-end model lifecycle, including training orchestration, experiment tracking, model registries, CI/CD, deployment and monitoring.
• EstablishMLOpsstandards and engineering practices, including model testing and validation, containerisation, deployment patterns, model packaging, release management and production operations.
• Establish standards for model artefact management, versioning, lineage and configuration control, ensuring model weights, parameters, hyperparameters and model cards are appropriately governed and traceable.
• Enable large-scale distributed training and, where appropriate, federated learning approaches, working closely with AI Infrastructure to provide scalable, efficient model-training capabilities and enable research to production velocity.
• Drive efficiency and cost optimisation across training and inference workloads, ensuring the AI Accelerator makes effective use of enterprise infrastructure.
• Establish ways of working and coach other team members, onboarding, mentoring and technically leadingMLOpsengineers as the team grows, while acting as the senior escalation point for complex MLOps challenges.

Requirements

• PhD orMSc and equivalent experience in a STEM subject.
• Extensive experience operating atsenior stafflevel within anMLOps, ML Platform Engineering or Machine Learning Engineering function with a proven track record of defining technical strategy, architecture and engineering standards for ML platforms and production AI systems, and with experience mentoring engineers and establishing engineering principles.
• Deep expertise across theMLOpslifecycle, including training orchestration, experiment tracking, model registries, CI/CD for machine learning, deployment, serving and monitoring of AI systems in production.
• Strong software engineering skills,includingproficiency in Python, and experience building scalable production-grade platforms and tooling, along with a deep understanding of containerisation and orchestration technologies such as Docker, Kubernetes and Helm.
• Strong collaboration and influencing skills, with the ability to work effectively across research, engineering and business teams.

This is a hybrid role with approximately 4 days a week in the office.

WHY THIS IS A GREAT PLACE TO WORK

Boehringer Ingelheim has been recognised as aTop Employer in the UK, demonstrating our commitment to building an exceptional workplace through strong people practices and supportive HR policies.