Senior ML Ops Engineer — Production AI Platforms (Hybrid)

Boehringer Ingelheim; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Hybrid work model

Top Employer in the UK

Job summary

Boehringer Ingelheim in London is seeking a senior ML Platform Engineer to own end-to-end model training, deployment and lifecycle management within the AI Accelerator. You will ensure provenance, robust registries, and efficient distributed training across compute resources.

The role requires collaboration with research and ML engineering teams, expertise in ML tooling, and strong CI/CD and infrastructure-as-code practices. Hybrid working model with in-office days in London.

Qualifications

• Postgraduate degree in ML/CS/SE or related field (PhD preferred or MSc with industry experience).
• Hands-on experience operating ML training and serving workflows in production.
• Experience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP or Ray Train.
• Experience operating experiment tracking systems and model registry systems such as MLflow, Weights and Biases or equivalent.
• Solid understanding of cloud infrastructure for ML, including compute, storage and networking, sufficient to specify requirements clearly and diagnose infrastructure-related issues.
• Experience working closely with research and ML engineering teams as a platform operator.
• Familiarity with CI/CD tooling for ML workflows, such as cloud-native pipeline services, GitHub Actions or equivalent.
• Awareness of large model training characteristics, including memory footprint, compute scaling and parallelisation strategies.
• Familiarity with infrastructure-as-code tooling such as Terraform or cloud-native equivalents.
• Familiarity with biomedical AI workloads, such as training foundation models on large-scale multimodal data.

Responsibilities

• Ensure experiment tracking and model registry systems are used effectively across the AI Accelerator, with consistent and correct logging of training and fine-tuning runs and model artefacts registered with full provenance.
• Configure, run and troubleshoot distributed training and fine-tuning jobs, ensuring efficient use of available compute and resolving job-level failures.
• Participate in structured model handovers with ML engineers, reviewing and signing off documentation before accepting full operational ownership of shipped models.
• Deploy, monitor and manage model serving endpoints, making technical decisions about serving configurations to meet downstream performance requirements.
• Take full operational ownership of models in production, managing monitoring, retraining and lifecycle end to end.
• Uphold MLOps standards and practices across the AI Accelerator, contributing to their evolution based on operational experience and keeping teams current with relevant advances in MLOps tooling.

Skills

Distributed training

Experiment tracking

CI/CD tooling

Cloud infrastructure for ML

MLOps

Model serving

Education

Postgraduate degree in ML/CS/SE or related (PhD preferred or MSc with industry experience)

Tools

PyTorch

Terraform

MLflow

Weights & Biases

GitHub Actions

Ray Train

Job description

Boehringer Ingelheim in London is seeking a senior ML Platform Engineer to own end-to-end model training, deployment and lifecycle management within the AI Accelerator. You will ensure provenance, robust registries, and efficient distributed training across compute resources.

The role requires collaboration with research and ML engineering teams, expertise in ML tooling, and strong CI/CD and infrastructure-as-code practices. Hybrid working model with in-office days in London.