Technical Expertise:

• Strong Python skills with the ML stack: NumPy, pandas, scikit-learn
• Hands-on experience with at least one deep learning framework (PyTorch, TensorFlow, JAX)
• Solid grounding in core ML—supervised, unsupervised, evaluation, regularisation, statistics
• Production ML experience—deploying, serving and monitoring models at scale
• LLM and generative AI tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows)
• MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent)
• Cloud experience (AWS, GCP, Azure), containerisation and basic data engineering / SQL
• Familiarity with experimentation, A/B testing and online evaluation is a plus

Engineering & Soft Skills:

• Strong software engineering discipline—models are products, not notebooks
• Rigour in evaluation, validation and reproducibility
• Scepticism about model behaviour, failure modes and limits
• Excellent communication—able to explain a model's behaviour to engineering, product and exec audiences
• Comfortable with ambiguity and iterative problem framing
• Curiosity about the underlying domain and the user problem the model is serving

Domain Flexibility:

• Roles span NLP, computer vision, recommender systems, forecasting, applied generative AI, fraud, personalisation, search and decisioning
• Background in any of these is welcomed; appetite to learn an adjacent domain valued just as much

Experience Level:

• Minimum 2+ years for AI / ML Engineer, 5+ for Senior, 8+ for Lead / Principal / Applied Scientist
• Background in machine learning engineering, applied science, data science with engineering focus, or research engineering
• Examples of models, pipelines or ML systems you've taken from idea to production