AI Engineer
Job Type: Fulltime
Location: Hyderabad / Hybrid
Experience: 4+ Years
No of positions: 1
Job Description:
AI Engineer / Developer (Azure + Azure Databricks)
We’re hiring an AI Engineer/Developer to design, build, and deploy AI solutions on Microsoft Azure and Azure Databricks (ADB). You will implement production-grade AI/GenAI use cases, from RAG-style knowledge assistants to AI-enabled analytics experiences, using Azure services and Databricks, with a strong focus on secure, scalable, and governed delivery. The role includes developing and operationalizing pipelines, integrating enterprise data sources, validating model outputs, and supporting pilot-to-production rollouts with CI/CD and MLOps practices.
What you’ll do
- Develop on Azure Databricks (with governed data/catalog patterns) and enable conversational/AI experiences on top of curated datasets using AI Genie.
- Build GenAI/LLM solutions using Azure OpenAI + Azure AI Search and integrate with enterprise content and permissions.
- Create AI/ML models for solving business use cases related to prediction, forecasting and risk mitigation using DBML, MLflow or Azure ML.
- Create deployment automation:CI/CD pipelines, MLOps setup, validation and monitoring, and support pilot rollout and iteration.
What you bring
- Strong hands-on engineering experience with Azure and Azure Databricks, including data pipelines and production deployment patterns.
- Experience building and integrating AI solutions (LLMs/RAG/MCP, embeddings, APIs) and grounding responses in enterprise data.
- Practical software engineering skills (Python) and comfort collaborating with data, security, and business stakeholders.
Preferred Technical Skills
- Azure Databricks (Lakehouse architecture)
- PySpark and SQL development
- Delta Lake (ACID tables, performance tuning)
- Medallion architecture (Bronze / Silver / Gold)
- Data ingestion using Auto Loader and Delta Live Tables (DLT)
- Unity Catalog (governance, metadata, lineage, RBAC)
- AI/ML model development (build, train, validate)
- Model retraining and lifecycle management
- MLflow for experiment tracking and model management
- CI/CD and MLOps for model deployment and operations
- Azure integration (ADLS Gen2, ADF, Azure services)
- Production rollout, monitoring, and operational support
