Senior AI/ML Engineer

Role Overview

• The Senior AI/ML Platform Engineer will build, productionize, and operate AI/ML solutions on Azure Databricks platform.
• The role will partner closely with Data Scientists and technology teams to move Machine Learning and Generative AI solutions from experimentation into reliable, scalable, and supportable production workloads.
• The primary focus is MLOps, model deployment and serving, production engineering, monitoring, and reusable AI/ML platform capabilities.

Key Responsibilities

AI/ML Engineering & Productionisation

• Partner with Data Scientists to productionize Machine Learning and Generative AI solutions.
• Build batch scoring, real-time inference, feature-engineering, and model-serving pipelines.
• Implement model lifecycle management including deployment, versioning, promotion, rollback, and retraining.
• Develop APIs and integration patterns that allow models to be consumed by applications and business workflows.
• Build the data and feature pipelines specifically required to support model training and inference.

MLOps & Platform Engineering

• Build and maintain reusable AI/ML deployment patterns using Azure Databricks, MLflow, Unity Catalog, Databricks Asset Bundles, and CI/CD.
• Automate model testing, deployment, and environment promotion across DEV, UAT, and PROD.
• Develop reusable templates and components that reduce one-off engineering across AMIG AI/ML initiatives.
• Support platform/runtime upgrades and continuously improve deployment and operational practices.

GenAI Engineering

• Productionize Generative AI solutions including RAG, semantic retrieval, knowledge-grounded applications, and agentic workflows where required.
• Integrate applications with approved enterprise LLM/model-access capabilities.
• Implement prompt, retrieval, grounding, and evaluation patterns.
• Apply appropriate LLM orchestration frameworks when needed without introducing unnecessary project-specific technology stacks.

Production Operations & Monitoring

• Implement monitoring for model performance, drift, data quality, inference services, and production workloads.
• Troubleshoot model scoring, data, endpoint, performance, and production issues.
• Support retraining, runtime/library upgrades, dependency management, and technical remediation.
• Develop runbooks and operational procedures and support the transition of new AI/ML solutions into production support.
• Optimize model-serving and Databricks workloads for reliability, performance, and cost.

Required Qualifications

• Minimum 10+ years of software, data, ML, or AI engineering experience, including hands-on experience with production ML solutions.
• Strong Python and SQL skills.
• Strong hands-on experience with Azure Databricks and Apache Spark.
• Experience with ML flow, model deployment, model lifecycle management, and CI/CD.
• Experience building batch and/or real-time inference pipelines.
• Experience with feature engineering and production ML architectures.
• Experience with Unity Catalog, Databricks Asset Bundles, Model Serving, and Databricks Apps.
• Experience integrating models with enterprise applications.
• Experience productionizing GenAI or RAG solutions.
• Understanding of production monitoring, troubleshooting, automated testing, and DevOps practices.
• Working knowledge of Generative AI, LLMs, RAG, embeddings, semantic/vector retrieval, and prompt engineering.
• Ability to work with Data Scientists and translate experimental models into production-ready solutions.

Preferred Qualifications

• Familiarity with enterprise LLM/model-access platforms and Azure AI services.
• Familiarity with LLM orchestration frameworks such as LangGraph, Semantic Kernel, or equivalent.
• Experience with AI/ML observability and model evaluation.
• Experience in insurance, financial services, or another regulated industry.