AI/ML Engineer / Data Scientist - Databricks

Job Title:AI/ML Engineer / Data Scientist - with Databricks focus
Experience: 8+Years
Work type: Remote (India)
Key Responsibilities:
• Develop, deploy, and maintain scalable MLOps pipelines for both traditional ML and Generative AI use cases leveraging Databricks (Unity Catalog, Delta Tables, Inference Tables, Mosaic AI).
• Operationalize large language models (LLMs) and other GenAI models, ensuring efficient prompt engineering, fine-tuning, and serving.
• Implement model tracking, versioning, and experiment management using MLflow.
• Build robust CI/CD pipelines for ML and GenAI workloads to automate testing, validation, and deployment to production.
• Use Vertex AI to manage training, deployment, and monitoring of ML and GenAI models in the cloud.
• Integrate high-quality, governed data pipelines that enable ML and Generative AI solutions with strong lineage and reproducibility.
• Design and enforce AI Governance frameworks covering model explainability, bias monitoring, data access, compliance, and audit trails.
• Collaborate with data scientists and GenAI teams to productionize prototypes and research into reliable, scalable products.
• Monitor model performance, usage, and drift — including specific considerations for GenAI systems such as hallucination checks, prompt/response monitoring, and user feedback loops.
• Stay current with best practices and emerging trends in MLOps and Generative AI.

Key Qualifications:
Must Have Skills:
• 3+ years of experience in MLOps, ML Engineering, or related field.
• Hands-on experience with operationalizing ML and Generative AI models in production.
• Proficiency with Databricks (Unity Catalog, Delta Tables, Mosaic AI, Inference Tables).
• Experience with MLflow for model tracking, registry, and reproducibility.
• Strong understanding of Vertex AI pipelines and deployment services.
• Expertise in CI/CD pipelines for ML and GenAI workloads (e.g., GitHub Actions, Azure DevOps, Jenkins).
• Proven experience in integrating and managing data pipelines for AI, ensuring data quality, versioning, and lineage.
• Solid understanding of AI Governance, model explainability, and responsible AI practices.
• Proficiency in Python, SQL, and distributed computing frameworks.
• Excellent communication and collaboration skills.

Nice to Have:
• Experience deploying and monitoring Large Language Models (LLMs) and prompt-driven AI workflows.
• Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG) architectures.
• Infrastructure-as-Code experience (Terraform, CloudFormation).
• Experience working in regulated industries (e.g., finance, Retail) with compliance-heavy AI use cases.