Machine Learning Engineer

Machine Learning Engineer (Contract) £500-600 per day 3 months (4 days per week) London (Hybrid, ~1 day per week) IR35: TBC We're working with an AI company in the trade and logistics space at a critical inflexion point, moving from enterprise pilots into production deployments. With strong ML capability already in place, they now need a senior contractor to come in and audit, structure, and productionise their existing ML systems. This role is focused on fixing and scaling what already exists, not building from scratch. The Role You will work directly with the technical co-founders to bring structure, scalability, and best practices across their ML and data setup, ensuring it is ready to support multiple enterprise deployments. Key responsibilities: • Audit current ML architecture, pipelines, and MLOps practices • Identify gaps in documentation, reproducibility, and scalability • Introduce clear structure across model development, versioning, and deployment • Improve training and retraining workflows • Strengthen model monitoring, evaluation, and performance tracking • Support productionisation and inference pipeline improvements • Ensure systems are maintainable for future hires • Provide a clear, pragmatic path from pilot to production You will also contribute to ongoing analytical work (risk modelling, anomaly detection), but the primary focus is on ML system structure and scalability. Technical Environment • High-volume ETL/ELT pipelines (multi-source ingestion: SAP, APIs, email, flat files) • Client-specific models built on shared base models (micro-model architecture) • Feedback-driven learning loops and model iteration • LLM workflows for document analysis (multi-prompt pipelines) • AWS and GCP infrastructure (Azure being introduced) • Containerised deployments with CI/CD • Multi-environment deployment (cloud, hybrid, on-prem) Your Skills & Experience • Strong Python and hands-on ML engineering experience • Proven experience in productionising ML systems end-to-end • Experience auditing and improving existing ML setups • Strong MLOps experience (model versioning, monitoring, CI/CD) • Experience working with data pipelines (ETL/ELT, Spark or similar) • Comfortable working with messy, real-world data and complex business logic • Experience in small, fast-moving teams (startup, scaleup, or consulting) • Strong communication and documentation skills Nice to have: • Experience with LLM workflows (RAG, LangChain, etc.) • Experience in logistics, trade, or compliance data • Experience supporting client-facing or pre-sales conversations To Apply: Please email Desired Skills and Experience Machine Learning Engineering, MLOps, ML System Auditing, Model Deployment, Retraining Pipelines, Model Versioning, Model Monitoring, CI/CD for ML, Python, Data Pipelines, ETL, ELT, Production ML Systems, Scalability, Documentation, Reproducibility, Cloud (AWS, GCP), Containerisation, LLM Workflows