#623 - AI/ML Engineer

BlueCloud is a Snowflake Elite Partner and the 2026 CoCo Catalyst Snowflake Partner of the Year. We help enterprise organizations move from fragmented legacy systems to unified, AI-ready Snowflake platforms — delivering data migration, engineering, governance, BI & analytics, and AI/ML solutions 40–50% faster than traditional approaches. With 450+ Snowflake consultants, 200+ enterprise transformations under our belt, and a 100% Snowflake focus, we combine advisory-led thinking with AI-powered accelerators to turn months of work into weeks of results. Our clients span Financial Services, Healthcare & Life Sciences, Retail, Manufacturing, Energy, and more — and the outcomes speak for themselves: 97% faster reports, 40% fraud reduction, $1.5M in client savings, and 10× client growth. We don't just strategize — we execute. Databricks AI/ML Engineer Design, develop, and deploy machine learning models on the Databricks platform. Build and operationalize end-to-end ML pipelines using Databricks, including feature engineering, model training, evaluation, and production deployment. Leverage Databricks MLflow for experiment tracking, model registry, and end-to-end ML lifecycle management. Experience with Databricks AI/BI Genie for conversational analytics, self-service insights, and business intelligence use cases. Hands-on experience with Databricks Agent Bricks for building, orchestrating, and operationalizing AI agents. Build agentic AI solutions on Databricks to support decision-making use cases, including rule-based alerts, anomaly detection, and automated insights delivery for business managers. Strong understanding and implementation experience with Generative AI solutions on Databricks, including RAG (Retrieval-Augmented Generation) and LLM-based applications. Collaborate with data engineering teams to design and implement scalable, high-performance data architectures on Databricks Lakehouse Platform. Work with large-scale structured and unstructured datasets using Databricks Delta Lake and Apache Spark. A good understanding of retail and CPG data domains is preferable.