Senior Machine Learning Engineer - AI / GenAI

SUMMARY:
Senior Machine Learning Engineer to deploy and support enterprise-scale Machine Learning

POSITION INFO:
Role Overview We are looking for an experienced Senior Machine Learning Engineer to design, build, deploy and support enterprise-scale Machine Learning, Artificial Intelligence and Generative AI solutions . The role is strongly focused on the productionisation and operationalisation of AI and ML solutions , including machine learning models, GenAI applications, AI agents, Retrieval-Augmented Generation (RAG) solutions and reusable ML platforms. The successful candidate will work extensively across Databricks, Azure Kubernetes Service (AKS), Kubernetes, Docker, MLflow and MLOps environments , partnering closely with Data Scientists, Cloud Engineers, Platform Teams, Security Teams and business stakeholders. This is an engineering-focused role requiring strong experience taking AI and Machine Learning solutions from prototype through deployment, scaling, monitoring and production support . Key Responsibilities Design, build, deploy and support production-grade Machine Learning and AI solutions . Productionise Machine Learning models and Data Science pipelines using Databricks . Develop and deploy Generative AI applications, AI agents and RAG solutions . Build reusable Machine Learning pipelines and frameworks using MLOps principles . Implement CI\/CD, automated testing, model monitoring, governance and deployment automation . Deploy and optimise open-source Machine Learning and Large Language Models within Azure Kubernetes Service (AKS) . Develop and support REST APIs and microservices that expose AI and Machine Learning capabilities to enterprise applications. Build scalable containerised solutions using Docker and Kubernetes . Implement and maintain Databricks Workflows, MLflow, Model Serving and Mosaic AI solutions. Monitor models for performance degradation, model drift, reliability and operational health . Troubleshoot production issues across models, ML pipelines, APIs, GenAI applications and supporting infrastructure. Optimise AI and ML platforms for performance, scalability, reliability and cost efficiency . Collaborate with Data Scientists to convert models and prototypes into business-ready production solutions . Partner with Cloud, Infrastructure, Security and Platform Engineering teams to ensure solutions comply with enterprise architecture and security standards. Contribute to reusable engineering frameworks, standards and best practices across the AI and Machine Learning ecosystem. Mentor junior engineers and support knowledge sharing within the engineering team. Core Technical Requirements Candidates should have strong hands-on experience in: Python SQL Databricks Databricks Workflows MLflow Databricks Model Serving Mosaic AI Microsoft Azure Azure Kubernetes Service (AKS) Kubernetes Docker REST API development Microservices CI\/CD MLOps Machine Learning Generative AI Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) AI Agents Spark \/ distributed computing Cloud-native AI\/ML platforms Monitoring and observability Infrastructure automation Required Experience The ideal candidate will have: Strong experience building, deploying and supporting Machine Learning solutions in production environments . Proven experience with MLOps, DevOps and software engineering practices within AI\/ML environments. Strong development experience using Python , together with SQL and API development. Hands-on experience with Databricks, MLflow and Model Serving . Strong experience with Kubernetes, Docker and containerised application deployment . Experience deploying Machine Learning and\/or Generative AI w