Key Responsibilities

• Lead discovery sessions to define analytical problems, success metrics, data requirements, experimentation plans, and production roadmaps.
• Explore and prepare structured and unstructured data; perform feature engineering, statistical analysis, and model selection using Python, SQL, Spark, and Databricks notebooks.
• Build, evaluate, and tune predictive, forecasting, optimization, natural language processing, computer vision, generative AI, and agentic AI solutions.
• Use Azure Databricks capabilities such as Delta Lake, Unity Catalog, MLflow, Feature Engineering, Model Serving, Mosaic AI, and vector search to create governed, production-ready solutions.
• Integrate solutions with Microsoft Azure AI Foundry, Azure OpenAI, Microsoft Fabric, OneLake, Power BI, and Azure services as appropriate.
• Implement MLOps and LLMOps practices including source control, automated testing, CI/CD, model registration, deployment, monitoring, drift detection, responsible AI, and cost optimization.
• Communicate findings and model behavior through clear visualizations, executive-ready narratives, demonstrations, and technical documentation.
• Own assigned workstreams, manage risks and dependencies, and collaborate with client teams to drive adoption and measurable business outcomes.
• Contribute to proposals, reusable accelerators, technical standards, peer reviews, mentoring, and the growth of Lantern’s Databricks and Microsoft AI practices.

Key Responsibilities

• Lead discovery sessions to define analytical problems, success metrics, data requirements, experimentation plans, and production roadmaps.
• Explore and prepare structured and unstructured data; perform feature engineering, statistical analysis, and model selection using Python, SQL, Spark, and Databricks notebooks.
• Build, evaluate, and tune predictive, forecasting, optimization, natural language processing, computer vision, generative AI, and agentic AI solutions.
• Use Azure Databricks capabilities such as Delta Lake, Unity Catalog, MLflow, Feature Engineering, Model Serving, Mosaic AI, and vector search to create governed, production-ready solutions.
• Integrate solutions with Microsoft Azure AI Foundry, Azure OpenAI, Microsoft Fabric, OneLake, Power BI, and Azure services as appropriate.
• Implement MLOps and LLMOps practices including source control, automated testing, CI/CD, model registration, deployment, monitoring, drift detection, responsible AI, and cost optimization.
• Communicate findings and model behavior through clear visualizations, executive-ready narratives, demonstrations, and technical documentation.
• Own assigned workstreams, manage risks and dependencies, and collaborate with client teams to drive adoption and measurable business outcomes.
• Contribute to proposals, reusable accelerators, technical standards, peer reviews, mentoring, and the growth of Lantern’s Databricks and Microsoft AI practices.