AI/ML Engineer (3 Years)

About the role As an AI/ML Engineer you'll deliver production-grade AI/ML solutions on Databricks for enterprise clients : building robust pipelines, productionise machine learning and Generative AI use cases, and helping clients move from notebook experimentation to reliable, governed, at-scale production systems. You'll work closely with data scientists, ML scientists, and solution architects, translating client requirements into reliable, governed, and scalable solutions. This role is the right fit for you if you love hands-on AI/ML engineering, direct client collaboration in consulting, and staying current with the latest in Databricks tooling, MLOps, and GenAI. What you'll do - Build and productionise feature pipelines and training/serving pipelines for ML and GenAI use cases, working with MLflow for experiment tracking and model management. - Support the delivery of Generative AI and agentic systems on Databricks, including data preparation for RAG pipelines, vector search integration, and AI/BI tooling such as Databricks Genie Spaces. - Help stand up new AI use cases end to end on Databricks, from data preparation through to a working prototype, working alongside data scientists to move ideas from notebook to production. - Build reusable Databricks templates and accelerators for common AI/ML use cases (RAG, agentic assistants, forecasting, personalisation), reducing time-to-development for new client engagements. - Optimise Databricks jobs, clusters, and workflows for cost, performance, and reliability at production scale. - Work directly with client engineering teams and stakeholders to gather requirements, troubleshoot issues, and hand over documentation and runbooks. About you - 3+ years of experience as an ML engineer, or similar, with meaningful hands-on time on the Databricks platform. - Strong Python and PySpark skills; comfortable writing production-quality Spark jobs, not just notebooks. - Practical experience in production Generative AI or agentic system delivery: RAG architectures, LLM orchestration frameworks (e.g. LangChain, LangGraph), AgentOps tooling (evaluation, monitoring, governance). - Extensive hands-on industry data science and ML experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc. - Background supporting large-scale consumer or enterprise ML systems (recommenders, ranking, forecasting, personalisation). - Experience with MLflow or a comparable tool for experiment tracking, model registry, and model serving. - Solid SQL skills and experience with cloud data platforms (Azure, AWS, or GCP — Azure Databricks experience is a plus). - Comfortable working directly with clients or cross-functional stakeholders to scope and deliver technical work. Nice to have - Databricks certifications (Machine Learning Associate/Professional, or Generative AI Engineer Associate). - Practical experience with Delta Lake and lakehouse/medallion architecture patterns - Working knowledge of Unity Catalog or equivalent data governance and access-control tooling - Prior consulting or professional-services experience in AI/ML delivery. - Familiarity with experimentation and A/B testing pipelines built on Databricks. - Exposure to causal inference or applied statistics (e.g. uplift modelling, Double ML) used to support AI use case development.