AVYAN AI

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.