Senior AI & ML Engineer

Senior AI & ML Engineer - MLOps - Terraform - Github Actions This role leads the design, deployment, and operation of scalable ML systems that support recommendation, forecasting, and automation services across digital and retail platforms. You will focus on MLOps, GenAI operations, Azure cloud engineering, and Snowflake data integration to ensure production-ready workflows. Your contributions will enhance reliability, observability, and performance of enterprise ML infrastructure. Develop end-to-end ML pipelines for data ingestion, training, validation, deployment, monitoring, and retraining.Operationalise GenAI models, including LLMs, embeddings, vector databases, and prompt orchestration, for enterprise use cases.Automate CI/CD workflows with GitHub Actions or Azure DevOps to ensure reproducibility, scalability, and governance.Implement model monitoring, observability, drift detection, and alerting using NewRelic, Azure Monitor, or custom frameworks.Build cloud-native architectures on Azure with services like Azure ML, AKS, and Azure OpenAI for scalability, high availability, and security.Integrate Snowflake for feature engineering, Snowpark, Python UDFs, real-time or batch pipelines, and data governance with lineage.Mentor ML and data engineers while collaborating with data scientists to productionise research models.Engage product and business stakeholders to align AI solutions with enterprise strategy and drive innovation in MLOps and GenAI. Essential qualifications comprise proven experience in ML platform engineering or MLOps with full lifecycle exposure, strong Python skills alongside SQL and Bash, hands-on work with MLflow, Scikit-learn, PyTorch, Airflow, FastAPI, Docker, Kubernetes, Terraform, and CI/CD tools. Familiarity spans Azure, AWS, Snowflake, Delta Lake, Redis, Azure Data Lake, feature stores, model registries, and enterprise ML governance. Desirable elements include multi-cloud AI/ML experience beyond Azure, open-source contributions to MLOps or GenAI frameworks, and deploying models as APIs with Azure Functions.