AI/ML Engineers - UK Wide
27 days ago
London
We work with a range of UK employers actively hiring across these roles \n About the Roles \n We're looking for experienced AI and Machine Learning Engineers at all levels—from AI Engineer and ML Engineer through to Senior, Lead, Applied Scientist and Research Engineer positions—for upcoming roles across applied machine learning, generative AI, MLOps and model productionisation. These are hands-on engineering roles where you'll build, train, ship and operate the models that drive real product behaviour and real business outcomes. \n You'll work across the full ML lifecycle—from problem framing and data preparation through to model development, evaluation, deployment, monitoring and iteration. The role suits someone who pairs solid software engineering foundations with genuine ML rigour and a healthy scepticism about what models can and can't be trusted to do. \n Key Responsibilities \n\n • Partner with product, data and engineering stakeholders to frame problems suitable for machine learning\n, • Build, train, evaluate and tune models across classical ML, deep learning and generative AI as appropriate\n, • Productionise models—APIs, batch pipelines, real-time inference, model registries\n, • Design and operate ML pipelines covering ingestion, feature engineering, training, evaluation and deployment\n, • Build robust evaluation harnesses—offline metrics, online experiments, guardrails and monitoring\n, • Apply LLMs and generative AI techniques where they're the right tool—RAG, fine-tuning, prompt engineering, agentic patterns\n, • Monitor models in production for drift, degradation, bias and safety concerns\n, • Partner with data engineering peers to build the feature and training data infrastructure ML needs\n, • (For Senior/Lead) Set ML architecture, mentor engineers and scientists, lead applied research initiatives\n\n What You'll Bring \n Technical Expertise: \n\n • Strong Python skills with the ML stack: NumPy, pandas, scikit-learn\n, • Hands-on experience with at least one deep learning framework (PyTorch, TensorFlow, JAX)\n, • Solid grounding in core ML—supervised, unsupervised, evaluation, regularisation, statistics\n, • Production ML experience—deploying, serving and monitoring models at scale\n, • LLM and generative AI tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows)\n, • MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent)\n, • Cloud experience (AWS, GCP, Azure), containerisation and basic data engineering / SQL\n, • Familiarity with experimentation, A/B testing and online evaluation is a plus\n\n Engineering & Soft Skills: \n\n • Strong software engineering discipline—models are products, not notebooks\n, • Rigour in evaluation, validation and reproducibility\n, • Scepticism about model behaviour, failure modes and limits\n, • Excellent communication—able to explain a model's behaviour to engineering, product and exec audiences\n, • Comfortable with ambiguity and iterative problem framing\n, • Curiosity about the underlying domain and the user problem the model is serving\n\n Domain Flexibility: \n\n • Roles span NLP, computer vision, recommender systems, forecasting, applied generative AI, fraud, personalisation, search and decisioning\n, • Background in any of these is welcomed; appetite to learn an adjacent domain valued just as much\n\n Experience Level: \n\n • Minimum 2+ years for AI / ML Engineer, 5+ for Senior, 8+ for Lead / Principal / Applied Scientist\n, • Background in machine learning engineering, applied science, data science with engineering focus, or research engineering\n, • Examples of models, pipelines or ML systems you've taken from idea to production\n\n What We Offer \n\n • Opportunity to work on applied AI where models genuinely change product behaviour and business outcomes\n, • Exposure to modern ML platforms, generative AI tooling and production MLOps practice\n, • Roles at the level you're ready for—we're hiring across the IC ML spectrum\n, • A collaborative environment where engineering rigour and modelling depth are both valued\n, • Clear scope to develop specialist depth (LLMs, vision, MLOps, applied research) or stay broad across ML problems\n, • Flexible working arrangements (on-site, hybrid or remote) and supportive team culture\n\n Job Type: Full-time \n Benefits: \n\n • Flexitime\n, • Work from home\n
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