Senior ML Engineer

Role overview A senior-level opportunity to design, build, and operate production machine learning and generative AI systems for enterprise and client-facing products. The role spans the full ML lifecycle, from data preparation through deployment and monitoring, with a focus on cloud-native platforms and MLOps excellence across AWS and Azure.

Responsibilities - Architect and ship scalable ML and generative AI solutions that run reliably in production. - Build end-to-end pipelines covering data preprocessing, feature engineering, training, evaluation, deployment, and ongoing monitoring. - Integrate large language models and AI services into enterprise applications and workflows. - Develop and tune models using frameworks such as Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, or Hugging Face. - Apply MLOps practices including CI/CD, model versioning, automated testing, observability, and governance. - Partner with data scientists, platform engineers, backend developers, product owners, and solution architects on releases and architecture decisions.

Requirements - A bachelor's or master's degree in computer science, data science, artificial intelligence, or a related field. - Six or more years of experience building and deploying ML systems. - Strong Python proficiency and hands-on work with AWS and/or Azure. - Experience with platforms such as SageMaker, Bedrock, or Azure ML and with REST APIs and distributed systems. - Solid grasp of the ML lifecycle, MLOps, CI/CD, and model monitoring.

Nice to have - Familiarity with Docker, Kubernetes, or Terraform. - Exposure to generative AI, LLMs, LangChain, LangGraph, or retrieval-augmented generation patterns.

Benefits and work setup - Remote UK contract, full-time, Monday through Friday. - Twenty-five days of holiday plus bank holidays, salary-sacrifice schemes including a tech purchase program, life assurance, private medical and dental cover, health assessments, cycle-to-work, gym discounts, and a referral scheme.