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Remote plus metro visibility increase applicants, but niche Databricks/MLOps/LLM requirements limit the pool.
Requires Azure Databricks, cloud MLOps and LLM expertise, making background fit highly domain-sensitive.
Multiple mandatory platform, MLOps, Databricks and LLM technical skills plus seniority require strict technical screening.
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.
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.
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.