AI Engineer

Job highlights

• Preferred Technical Skills
• 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

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