Job Description

MLOps Engineer
London
Contract
Competitive
PCD300004

About The Role
The organisation is a global business and technology consultancy seeking an MLOps Engineer to work for our client within the retail sector. This is initially a 6-month contract with the potential to extend and will be mainly remote with travel to their London office once a month.
Our client is seeking an MLOps Engineer to work on Ad-tech, interpreting and following architectural and engineering principles, operating frameworks, and new and improved technology and solutions. With your technical craft, curiosity and experimentation, you’ll use judgement to apply specific techniques to deliver focused outcomes that support our customers.
The client builds AI/ML data products that underpin key commercial decisions across the business, bring intelligent automation at scale to operational systems, and build out AI/ML microservices that power colleague and customer-facing applications. As an MLOps Engineer, you will support these products from inception. This requires working across the full data ecosystem: developing application-specific data pipelines (features), building CICD pipelines that automate the training and deployment of machine learning models, publishing the model results for downstream consumption, and/or building out the APIs that serve model outputs to downstream systems on-demand.

About You
Strong programming skills in Python; experience with ML libraries such as data and machine-learning framework
Experience with containerization tools like containerisation tooling and orchestration tools like workflow orchestration tooling or workflow orchestration tooling
Familiarity with cloud platforms (public cloud platform) and ML services (e.g., cloud-native service, cloud-native service).
Experience with CI/CD pipelines and automation tools like source-control and CI/CD tooling.
Understanding of monitoring and logging tools (e.g., monitoring and observability tooling).

Responsibilities:
Design, implement, and maintain scalable ML model deployment pipelines (CI/CD for ML).
Build infrastructure to monitor model performance, data drift, and other key metrics in production.
Develop and maintain tools for model versioning, reproducibility, and experiment tracking.
Optimise model serving infrastructure for latency, scalability, and cost.
Automate the end-to-end ML workflow, from data ingestion to model training, testing, deployment, and monitoring.
Collaborate with data scientists to ensure that models are production-ready.
Implement security, compliance, and governance practices for machine learning systems.
Support troubleshooting and incident response for deployed ML systems.

Preferred:
Prior experience deploying ML models in production environments.
Knowledge of infrastructure-as-code tools like infrastructure-as-code tooling or infrastructure-as-code tooling
Familiarity with model interpretability and responsible AI practices.
Experience with feature stores and model registries.