Job Description

JOB RESPONSIBILITIES:

Design, build, and maintain data pipelines using Databricks (Lakeflow, Declarative Pipelines, Asset Bundles). Develop scalable data engineering solutions across the data lake and analytics ecosystem. Build and deploy Python-based applications, including integration with AI Agents and machine learning workflows. Implement MLOps best practices for model development, deployment, monitoring, and lifecycle management. Develop and manage microservices and API solutions, including deployment within Kubernetes environments. Collaborate with cross-functional teams to build and optimize data models and analytics solutions. Create, maintain, and optimize Power BI dashboards and reports for business stakeholders. Ensure data quality, performance optimization, and adherence to security/compliance requirements.

JOB QUALIFICATIONS:

Strong experience with Databricks (including Lakeflow, Declarative Pipelines, and Asset Bundles). Solid background in Python development, particularly for data engineering and AI-related projects. Understanding of AI Agents or agent-based application development (theoretical or practical exposure). Hands-on experience with MLOps tools, CI/CD pipelines, and model deployment workflows. Experience deploying services/applications in Kubernetes (AKS/EKS/GKE or similar). Proficiency in Power BI for data visualization and dashboard development. * Knowledge of data architecture patterns, version control (Git), and cloud platforms (Azure preferred).