We’re working with a client who is building out core AI/ML and MLOps capabilities to better support data science teams across commercial, manufacturing, and quality use cases.
They’re looking for an experienced AI/ML Platform Engineer to help design and implement the foundations, covering experimentation, training environments, data pipelines, and deployment workflows. This is a hands-on contract role with a strong focus on delivery.
What you’ll be working on
* Setting up and improving MLflow for experiment tracking and model registry
* Building and supporting AWS SageMaker environments for model training and experimentation
* Developing ETL / data ingestion pipelines, using tools such as Dagster or Argo
* Putting in place standard MLOps tooling, including CI/CD and container-based workflows
* Working with PostgreSQL + pgvector to support vector and embedding-based use cases
* Building secure APIs to integrate ML services with existing enterprise systems.
Future work
Depending on priorities, this may extend into:
* Feature store work
* Model and data repositories
* Synthetic data generation
* Model evaluation and monitoring frameworks
* Explainability tooling
Essential
* Strong experience in MLOps / ML platform engineering
* Solid Python skills
* Hands-on experience with AWS, particularly SageMaker and S3
* Experience with Docker and CI/CD pipelines
* Background building or maintaining ETL / orchestration workflows
* Experience using MLflow or a similar experiment tracking/ model registry tool
Nice to have
* Experience with vector databases or embeddings
* PostgreSQL experience
* Exposure to feature stores or model lifecycle tooling
Additional info
* Hybrid working: 1 day per week on-site in London
* Outside IR35
* Quick turnaround with a single-stage interview process
* Rate: £900 per day (Outside IR35)
* Start Date: ASAP
* Interview Process: 1-stage interview