Machine Learning Engineer

• • ML Applied Engineer – 6 month initial contract (temporary) – outside IR35 – London – day rate dependent on experience • • We are looking for a senior, hands-on Applied ML Engineer to bridge the gap between classical data science and generative AI. This is an initial 6-month contract, with the possibility of a Phase-2 extension subject to successful delivery of first phase 1. The role is focused on delivery, execution, and technical ownership. You will be brought in to accelerate the product roadmap, transitioning capabilities from experimentation into robust, enterprise-grade systems embedded directly into our products and client solutions. You will be tasked to create Hybrid AI systems: using LLMs to provide interaction and explanation, grounded strictly by quantitative ML models (cost-to-serve, forecasting, and optimisation). You will own the transition from experimental notebooks to enterprise-grade solutions. Key responsibilities and deliverables: AI & Machine Learning Solution Delivery • Take end-to-end ownership of AI and ML solutions, from architecture and build through to production deployment. • Design and implement generative AI, classical ML, and hybrid systems, combining LLMs with predictive and analytical models. • Develop solutions that support forecasting, classification, optimisation, and recommendation alongside generative capabilities. • Translate complex business problems into practical AI/ML implementations that enhance analytics and decision workflows. Model Development & Optimisation • Lead hands-on development, training, fine tuning, and optimisation of machine learning and generative models • Build and deploy predictive models (e.g. demand, cost drivers, performance indicators) to support decision intelligence. • Apply techniques such as supervised learning, feature engineering, model calibration, and explainability. • Develop specialised language models and RAG-based systems aligned to performance management and cost-to-serve use cases. AI Infrastructure, ML Engineering & MLOps • Design and implement AI / ML pipelines covering training, deployment, versioning, monitoring, and retraining. • Establish monitoring for model drift, data quality, performance degradation, and bias. • Work with IT to deploy models using containerised architectures and CI/CD pipelines. • Leverage Microsoft Azure, SQL based systems, and cloud infrastructure to support large scale inference and data processing. Evaluation & Quality Assurance • Build evaluation frameworks for both ML models and generative AI systems. • Define and track metrics such as prediction accuracy, stability, latency, and business impact. • Validate AI generated outputs against ML driven benchmarks and ground truth data. • Ensure outputs meet enterprise standards for trust, explainability, and decision support. Data Engineering & Governance • Partner with internal teams to ensure access to high quality, governed datasets for ML training and inference. • Oversee data preprocessing, feature engineering, enrichment, and augmentation. • Apply strong governance, privacy, and security controls across AI and ML workflows. Rapid Prototyping, Product Integration & Handover • Rapidly prototype AI and ML features, including predictive tools, optimisation engines, and decision advisors. • Test solutions in live data and iterate quickly based on feedback. • Convert successful prototypes into production ready capabilities adopted by our internal teams first but with the intention to deploy to clients • Integrate AI and ML outputs into products, dashboards, and reporting layers. • Document models, assumptions, architectures, and operational processes to support handover at contract end. Essential Skills and experience: • Strong experience delivering machine learning and/or generative AI solutions in production • Hands on expertise in Python and ML/AI frameworks. • Experience fine tuning and customising LLMs using modern techniques such as LoRA/QLoRA • Experience building predictive, classification, or optimisation models for real world business problems. • Strong understanding of MLOps, model monitoring, and production deployment. • Ability to balance model performance with interpretability and business trust. • Strong understanding of MLOps, model monitoring, and production deployment is desirable but not essential.