Senior Software Engineer - Data & Infrastructure

6-MONTH CONTRACT

£450-£550 PER DAY (OUTSIDE IR35)

This role offers a great opportunity for a Software Engineer to join a high-performing data function building out infrastructure for cutting-edge ML and LLM use cases. You'll work closely with Data Engineers and Data Scientists to design clean, production-ready systems that support model training, fine-tuning, and deployment. The environment values autonomy, quality, and technical curiosity, with the freedom to shape best practices in a fast-scaling ML context.

This is a tech-forward business investing heavily in AI and machine learning, with a focus on scalable systems and real-world impact. With a modern data platform already in place, they're now scaling up their ML/LLM capabilities and need a strong software engineer to help operationalise model development workflows. You'll be joining a collaborative, cross-functional team working at pace to solve complex technical challenges.

You'll sit at the intersection of data and machine learning, building services and tooling that bridge raw data with production-grade ML pipelines. From preparing high-quality training data to designing APIs for model inference, you'll help enable the full lifecycle of ML and LLM deployment.

Your responsibilities will include:

• Building backend services and APIs to support data preparation and model training workflows.

• Collaborating with Data Scientists and Engineers to streamline LLM/ML experimentation and deployment.

• Implementing tools for dataset validation, monitoring, and reproducibility.

• Supporting the development of real-time and batch inference pipelines.

• Contributing to the scalability, efficiency, and reliability of ML infrastructure.

• Strong experience in Python, Java, or Scala for backend development.

• Solid understanding of data processing and engineering workflows.

• Experience building APIs or services to support data or ML applications.

• Familiarity with ML model lifecycle and tooling (e.g. MLflow, Airflow, Docker).

• Strong problem-solving skills and the ability to work autonomously in a dynamic environment.

• Experience supporting LLM training or retrieval-augmented generation (RAG).

• Familiarity with feature stores, vector databases, or embedding pipelines.

• Understanding of data quality, lineage, and governance best practices.

• Exposure to cloud platforms (AWS, Azure, or GCP) and distributed compute environments.