Senior Machine Learning Engineer - AI / GenAI
Core
Design, build, deploy, and support enterprise-scale Machine Learning, AI, and Generative AI solutions, focusing on productionization and operationalization of models, GenAI applications, AI agents, and RAG solutions.
Role type
Senior Machine Learning Engineer (GenAI/MLOps)
Builds
Production-grade ML models, GenAI applications, AI agents, RAG solutions, and reusable ML platforms on Azure.
Domain
Enterprise AI/ML, Generative AI, Cloud Infrastructure
Deliverable
production ML models
Required skills
Python, SQL, Databricks, Databricks Workflows, MLflow, Databricks Model Serving, Mosaic AI, 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
Preferred skills
Experience mentoring junior engineers, knowledge of enterprise architecture and security standards
Technologies
Databricks, Azure Kubernetes Service (AKS), Kubernetes, Docker, MLflow, Mosaic AI, Spark
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.
