Senior Data Scientist – GenAI​/RAG

Job Type :
Full-time

Strong hands-on experience in Agentic AI and Multi-Agent Systems

Experience with Lang Graph, Lang Chain, MCP (Model Context Protocol), tool calling, agent orchestration

Strong RAG implementation experience including Vector Databases, Hybrid Retrieval, Reranking, Knowledge Graphs

Hands-on experience with AWS Bedrock and enterprise GenAI solutions

Strong Python and SQL skills

Solid Traditional Data Science / Machine Learning background:

• Classification
• Regression
• Forecasting
• Anomaly Detection

Experience with XGBoost, Cat Boost, Random Forest, Deep Learning frameworks (PyTorch/Tensor Flow)

Experience designing and deploying production AI/ML systems

Understanding of MLOps / LLMOps, model monitoring, evaluation, observability, and retraining pipelines

Ability to translate business problems into ML or Agentic AI solutions

Experience with LLM Evaluation, Hallucination Detection, Groundedness and Retrieval Quality metrics

Exposure to Databricks, Spark, Vector Databases, APIs, Cloud Platforms (AWS preferred)

• Strong proficiency in programming languages such as Python or R.
• Experience with machine learning frameworks (e.g., Tensor Flow, PyTorch, Scikit-learn).
• Solid understanding of statistical analysis techniques and data modeling.
• Proficiency in data visualization tools (e.g., Tableau, Power BI).
• Familiarity with big data technologies (e.g., Hadoop, Spark).
• Ability to work with databases and query large datasets using SQL.
• Strong problem-solving skills and the ability to think critically.
• Excellent communication and collaboration skills.

• Build and deploy RAG (Retrieval-Augmented Generation) systems & AI chat interfaces
• Work closely with client data science teams (ML/DL ecosystems)
• Develop GenAI-based enterprise knowledge solutions
• Collaborate directly with stakeholders and customers
• Develop and implement machine learning algorithms to solve complex business problems.
• Analyze large datasets to generate insights and inform decision-making processes.
• Collaborate with product managers and engineers to integrate data science solutions into enterprise products.
• Communicate findings and recommendations effectively to technical and non-technical stakeholders.
• Stay current with the latest advancements in data science and machine learning technologies.