▷ Urgent Search: Data Scientist- R01556764

Job Description Data Scientist Primary Skills - Data Science skill - Machine Learning, NLP, and LLMs - Databricks Experience- 5 to 8yrs Specialization - Data Science Advanced: Generative AI & Databricks Job requirements - Job Title: Senior AI Engineer Generative AI & Databricks - Experience: 5-8+ Years - About the Role - We are seeking a Senior AI Engineer with strong expertise in Generative AI (GenAI), Databricks, and end-to-end ML/LLM systems. - You will be responsible for designing, building, and deploying intelligent, scalable GenAI solutions integrated into enterprise-grade data and analytics platforms. - The ideal candidate combines strong software engineering, MLOps, and LLM engineering experience with the ability to lead AI agentic workflows, data pipeline optimization, and model-driven automation using Databricks, MLflow, and Azure/Snowflake ecosystems. - Key Responsibilities - 1. Solution Architecture & Implementation - Design and implement end-to-end Generative AI solutions on Databricks, leveraging Unity Catalog, MLflow, Delta Lake, and Vector Search. - Architect LLM-based multi-agent frameworks for intelligent automation, chatbot systems, and document reasoning tasks. - Integrate Cortex AI, OpenAI, or Anthropic APIs for retrieval-augmented generation (RAG), conversational reasoning, and workflow orchestration. - 2. Model Development & Optimization - Fine-tune and evaluate LLMs and domain-specific NLP models (NER, Risk Assessment, Question Answering). - Develop pipelines for prompt engineering, context management, model evaluation, and hallucination detection. - Optimize inference performance, latency, and cost across multi-cloud and Databricks environments. - 3. Data Engineering & Governance - Collaborate with data engineering teams to ensure clean, well-governed, and vectorized data pipelines. - Build and maintain feature stores and embeddings stores using Databricks or Snowflake. - Implement data validation, lineage, and monitoring using Delta Live Tables and Unity Catalog. - 4. MLOps & Automation - Build reusable ML pipelines using Databricks Repos, MLflow, and Feature Store. - Automate deployment, monitoring, and retraining workflows for continuous model improvement. - 5. Collaboration & Leadership - Partner with product managers, data scientists, and business stakeholders to translate ideas into production-ready AI systems. - Review code, mentor junior engineers, and enforce best practices in scalable AI/ML development. - Contribute to internal knowledge bases, documentation, and reusable component libraries. - Required Skills & Expertise - Core AI/ML - Strong background in Machine Learning, NLP, and LLMs (Transformers, RAG, embedding models). - Proven experience fine-tuning or implementing models using Hugging Face, LangChain, LlamaIndex, or OpenAI API. - Knowledge of retrieval-augmented generation, multi-agent orchestration, and context management. - Databricks & Cloud Ecosystem - Expertise in Databricks (Delta Lake, MLflow, Unity Catalog, Feature Store, Vector Search). - Familiarity with Azure Databricks, Azure OpenAI, or Snowflake Cortex AI. - Experience integrating external APIs and cloud-native microservices (FastAPI, REST, or gRPC). - Programming & Engineering - Strong proficiency in Python, SQL, PySpark, and Databricks Notebooks. - Experience building modular codebases, deploying APIs, and working with CI/CD pipelines (GitHub Actions, Azure DevOps). - Hands-on experience with Streamlit, Gradio, or other UI frameworks for AI app development. - MLOps & Validation - Hands-on with MLflow tracking, model registry, and experiment management. - Experience in AI validation, faithfulness scoring, drift detection, and integrity match metrics. - Working knowledge of Docker, Kubernetes, and inference scaling techniques. - Soft Skills - Strong communication, stakeholder management, and ability to translate business problems into AI solutions. - Comfort working in agile, multi-disciplinary environments. - Passion for innovation, experimentation, and applied AI problem-solving. Mandates - - Need GenAI Data Scientist Databricks certified ML Engineer and work closely with customers. - Use case will involve data extract from pdf-based documents. - Leverage Databricks native solutions.