Company Profile - Celebal Technologies a Premier Microsoft Azure partner and have been recently funded by Norwest Venture Capital. Celebal Technologies is a premier software services company in the field of Data Science, Big Data, Enterprise Cloud & Automation. Established in 2016, in this short span of time we grew to headcount of 2200+ We help in achieving a competitive advantage with intelligent data solutions, built using cutting-edge technology. Objectives for Senior Data Scientist-GenAI: 1. Design and Develop LLM-Based Solutions: Create and deploy Generative AI solutions using LLMs, including RAG architectures, natural language querying of structured data, and enterprise content generation across diverse customer datasets. 1. Optimize and Productionize ML Workloads: Build, scale, and refine customer-facing machine learning and GenAI workloads, ensuring seamless deployment through best-in-class MLOps practices tailored to enterprise environments. 1. Advise on Architecture and Best Practices: Provide strategic guidance to data science teams on solution architecture, technology stack selection, and implementation best practices to ensure scalable and maintainable AI solutions. 1. Drive Cross-Industry Impact: Apply deep ML and GenAI expertise to address complex business challenges across industries such as Healthcare, Finance, Retail, and Startups, delivering high-value outcomes. 1. Mentor and Upskill Teams: Offer technical mentorship to internal and customer ML teams, contributing to knowledge sharing and capability building within the broader ML and AI/GenAI community. 1. Lead Thought Leadership Initiatives: Represent the organization at major conferences (e.g., Data + AI Summit) through speaking engagements and knowledge contributions, positioning the team as an industry leader in GenAI. 1. Collaborate with Product & Engineering Teams: Engage closely with product managers and engineers to align GenAI solution development with platform capabilities, provide feedback, and influence the product roadmap. ➢ Minimum 5+ years of hands-on data science experience, implementation experience as a customer facing consultant as well as proven track record in project delivery, preferably across multiple industries in: • ML/AI (must have experience in productionized models) • GenAI • NLP • Infrastructure management skills (docker, pipeline management, databases, etc). • LLMs 2 Cloud Services ➢ Hand on Experience with Databricks on one or more modern cloud platforms, including their data services: • AWS • Azure • GCP • Experience in integrating cloud services with Databricks for data ingestion, storage, and compute. • Familiar with CI/CD and infrastructure-as-code tools (e.g., Docker, Kubernetes, Terraform) 3 Programming • Strong in Python (pandas, scikit-learn, numpy) • Hands-on with ML/DL frameworks: TensorFlow, PyTorch • LangChain • Hugging Face • MLflow • OpenAI APIs for LLM applications • SQL 4 Databricks Platform ➢ Hands-on experience with • End-to-end project on Databricks (data engineering + ML/GenAI workflows). • Databricks notebook environment and job scheduling • Delta Lake • Apache Spark • MLflow • Workspace Management 5 Migration Architecture • Hand on experience in designing or implementing migration of ML models to Databricks. • Understanding of RAG architecture, chunking, Agents, Text2SQL vector DB integration (e.g., FAISS, Chroma). 6 Deployment Strategy ➢ Hand on Experience with • CI/CD pipeline implementation (GitHub Actions, Azure DevOps, etc.) in Databricks environment • Deploying GenAI applications (chatbots, LLMs) in production environments. 7 Projects & Client Delivery • Hand on Experience in delivering client-facing GenAI/ML projects. • Experience with business problem solving, presenting solutions, documenting end-to-end workflow, articulate evaluation metrics & optimization strategies • Experience preparing and delivering technical demonstrations or assignments