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 recent 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.

📌 Senior AI/ML Engineer (Canada)
🏢 Envision Technology Solutions
📍 Canada