AI/ML Engineer (Databricks, MLOps, MLflow, AutoML)

We're hiring multiple AI/ML Engineer with strong Databricks experience to design, build, and deploy machine learning solutions at scale. You'll work across data engineering, data science, and platform teams to deliver production-ready models using the Databricks ecosystem.

My client is a Databricks Partner consultancy, we're looking for multiple contractors to join on an initial 6 months engagement, fully remote and Outside of IR35.

Tech Stack: Databricks (core platform), Apache Spark/PySpark , MLops, MLflow, AutoML, Feature Store, Model Serving, Delta Lake, * Build and deploy ML models using Databricks (ML, Workflows, Feature Store)

• Develop scalable pipelines using Apache Spark (PySpark)
• Train, evaluate, and optimise models for real-world use cases
• Implement MLOps best practices (CI/CD, monitoring, versioning)
• Work with large-scale data in Delta Lake
• Collaborate with data engineers to productionise pipelines
• Deploy models via APIs or batch scoring workflows
• Ensure models are reliable, explainable, and performant
• Contribute to architecture decisions across the data platform

• Strong hands-on experience with Databricks
• Proven ML experience (classification, regression, NLP, or similar)
• Solid Python skills (Pandas, NumPy, Scikit-learn)
• Experience with PySpark/Spark
• Experience deploying ML models into production environments
• Understanding of MLOps frameworks (MLflow, CI/CD pipelines)
• Experience working with cloud platforms (AWS, Azure, or GCP)
• Strong SQL and data modelling knowledge

Desirable Experience

• Experience with Databricks MLflow and Feature Store
• Exposure to LLMs/GenAI (eg RAG pipelines, fine-tuning)
• Experience with streaming data (Kafka, Spark Streaming)
• Knowledge of Docker/Kubernetes
• Experience with Azure ML or SageMaker
• Familiarity with data governance and security best practices

What We're Looking For

• Someone who can bridge data engineering and data science
• Comfortable working in a production-focused environment
• Strong communicator who can work with technical and non-technical stakeholders
• Pragmatic mindset - focused on delivering business value, not just models