Lead/Sr AI Engineer with Azure Data Bricks || Immediate Joiner
Enable Data (company site, opens in a new tab)BengaluruRemoteDirect career pageEst. 28–45 LPA
Posted on 18 Sept
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Job description
Structured overview of role & requirements
About This Role
- Develop, deploy, and maintain production-ready AI applications on Azure Databricks and Azure cloud, ensuring scalability, reliability, and cost-efficiency.
- Design end-to-end data and AI pipelines integrating ML and Generative AI models, including LLM fine-tuning and vector search implementations.
- Collaborate with data scientists and platform engineers to move AI models from experimentation to production, implementing CI/CD, monitoring, logging, and governance.
Minimum Requirements
- Hands-on experience with Azure Databricks (jobs, workflows, clusters, Unity Catalog preferred), Python (PySpark), SQL, and Azure services including ADLS Gen2, ADF, Key Vault, IAM concepts.
- Strong understanding of ML lifecycle, MLOps best practices, and model deployment frameworks such as MLflow.
- Experience with CI/CD pipeline creation and orchestration using Azure DevOps or similar tools for deploying Databricks workflows.
- Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Experienced in building and optimizing data and AI pipelines specifically on Azure Databricks with PySpark-heavy workloads.
- Skilled in deploying and fine-tuning Generative AI models including LLMs, with knowledge of tools like LoRA/QLoRA, Azure OpenAI, Hugging Face, and vector search technologies (FAISS, Azure Cognitive Search).
- Familiar with infrastructure as code (Terraform/ARM/Bicep), AI safety guard rails, prompt engineering, and collaborative cross-functional project delivery in cloud environments.