GenAI & Databricks- R01556764

Data Scientist

• Data Science skill - Machine Learning, NLP, and LLMs
• Databricks

• Data Science Advanced: Generative AI & Databricks

• 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.

• • 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.