Data Scientist

Job Summary:

We are seeking an experienced and highly motivated Senior Data Scientist with a strong background in Generative AI, agentic systems, and end-to-end deployment of AI solutions. This role demands proficiency not only in building intelligent systems but also in operationalizing them using MLOps best practices and cloud-based deployment strategies. The ideal candidate will thrive at the intersection of data science, software engineering, and infrastructure.

Key Responsibilities:

• Design, develop, and deploy advanced AI-powered chatbots and agent-based solutions using frameworks like LangChain and LangGraph.
• Build and maintain backend APIs and user-facing interfaces using FastAPI and Streamlit.
• Leverage Snowflake for data integration, transformation, and analytics.
• Implement and manage MLOps pipelines for model training, validation, versioning, and monitoring.
• Deploy models and applications on AWS and GCP, ensuring scalability, reliability, and security.
• Use Redis for caching and real-time data access in production-grade applications.
• Collaborate with cross-functional teams to translate business needs into technical solutions.
• Prepare and deliver effective presentations to stakeholders and leadership teams.
• Ensure code quality, testing, and documentation in Python environments.

Required Skills:

• 5–8 years of hands-on experience in Data Science, with a focus on deployment.
• Proven experience in Chatbot development, Agentic AI, and LLM-based frameworks.
• Strong Python programming skills, including use of PyTorch, LangChain, and LangGraph.
• Proficient in MLOps practices: CI/CD for ML, model monitoring, version control, and automated retraining.
• Experience deploying solutions on cloud platforms (AWS, GCP).
• Backend and app development experience with FastAPI, Streamlit, and Redis.
• Strong working knowledge of Snowflake and data engineering workflows.
• Excellent communication and presentation skills, with the ability to convey technical topics to non-technical audiences.
• Experience with containerization tools like Docker and orchestration with Kubernetes.
• Exposure to prompt engineering and LLM fine-tuning.

Nice to Have:

• Familiarity with tools like MLflow, DVC, Vertex AI.
• Experience working in agile product teams or startup environments.