AI Platform Engineer

Aspire Technology; England; Hybrid; GBP 80,000 - 100,000; Full time

Job summary

A technology company is seeking an experienced AI / ML Platform Engineer to design and implement foundations for MLOps. Responsibilities include setting up MLflow, building AWS SageMaker environments, and developing ETL pipelines. The ideal candidate will have strong experience in MLOps, solid Python skills, and hands-on expertise with AWS tools. This is a contract role based in the UK, offering hybrid working, outside IR35, with a competitive rate of £900 per day.

Qualifications

• 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.

Responsibilities

• 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.
• Putting in place standard MLOps tooling, including CI / CD and container-based workflows.
• Building secure APIs to integrate ML services with existing enterprise systems.

Skills

MLOps

Python

AWS (SageMaker, S3)

Docker

ETL workflows

MLflow

Tools

AWS SageMaker

PostgreSQL

Dagster

Argo

Job description

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