Senior Engineer-Azure+AI

7-10+ years

(Team is working on - Azure Data lake on data bricks platform)

Data scientist role creating value using AI (hybrid of DS and DE, with SCM planning as a functional – highly preferred)

Technically – proven exp databricks app, and lakebase (OLTP in the platform – used for maintaining information) application.

Exp with AI solution using Genie Code and Genie Space.

Key Responsibilities:

• Design, build, and support enterprise data, analytics, AI, and machine learning solutions using the Databricks Lakehouse Platform
• Design and Develop scalable data pipelines, AI/BI dashboards, Databricks Apps, Lake base applications, and data products to meet business and analytical requirements.
• Collaborate with business stakeholders, architects, and product teams to define functional and technical solution requirements.
• Design enterprise data architectures, integrations, and workflows that align with governance, security, and performance standards.
• Develop and operationalize machine learning, Generative AI, RAG, and agentic AI solutions using Databricks AI capabilities.
• Deliver trusted analytics and insights through governed data products, dashboards, and self-service analytics.
• Evaluate existing application ecosystems (including Oracle Supply Chain Planning and S&OP) and recommend modernization opportunities using Databricks.
• Lead solution design, implementation, testing, support, and continuous improvement while ensuring best practices for scalability, reliability, and cost optimization

*Qualifications and Competencies

Experience:

• 5+ years of experience designing and delivering enterprise data, analytics, AI, or engineering solutions.
• Proven experience with the Databricks Lakehouse Platform, including Unity Catalog, Delta Lake, Databricks SQL, AI/BI Dashboards, Databricks Apps, Lakebase, Workflows, and MLflow.
• Hands-on experience building production-grade GenAI, RAG, multimodal AI, agentic workflows, Vector Search, and MLOps solutions using Databricks, Azure OpenAI, AWS, Azure, or GCP.
• Strong proficiency in PySpark, SQL, Python, data engineering, machine learning, and cloud-native architectures.
• Experience with enterprise application integration, Agile delivery, and the full software development lifecycle.
• Excellent stakeholder management, problem-solving, and communication skills.