This role is for a Machine Learning Engineer on a 6-month contract in London, offering a day rate based on experience. Key skills include Python, ML/AI frameworks, and experience with generative AI. MLOps knowledge is desirable. •• ML Applied Engineer – 6 month initial contract (temporary) – outside IR35 – London – day rate dependent on experience•• 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, 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.

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