Senior Data Scientist

Must Have Qualifications: Must have hands on experience with machine learning transitioned into GenAI. Rag, Python- Jupyter, other Software knowledge, using agents in workflows, strong understanding of data. Preferred: Built AI agent, MCP, A2A, Graph Rag, deployed Gen AI applications to production. Responsibilities • Architect and implement scalable AI Agents, Agentic Workflows and GenAI applications to address diverse and complex business use cases. • Develop, fine-tune, and optimize lightweight LLMs; lead the evaluation and adaptation of models such as Claude (Anthropic), Azure OpenAI, and open-source alternatives. • Design and deploy Retrieval-Augmented Generation (RAG) and Graph RAG systems using vector databases and knowledge bases. • Curate enterprise data using connectors integrated with AWS Bedrock's Knowledge Base/Elastic • Implement solutions leveraging MCP (Model Context Protocol) and A2A (Agent-to-Agent) communication. • Build and maintain Jupyter-based notebooks using platforms like SageMaker and MLFlow/Kubeflow on Kubernetes (EKS). • Collaborate with cross-functional teams of UI and microservice engineers, designers, and data engineers to build full-stack Gen AI experiences. • Integrate GenAI solutions with enterprise platforms via API-based methods and GenAI standardized patterns. • Establish and enforce validation procedures with Evaluation Frameworks, bias mitigation, safety protocols, and guardrails for production-ready deployment. • Design & build robust ingestion pipelines that extract, chunk, enrich, and anonymize data from PDFs, video, and audio sources for use in LLM-powered workflows—leveraging best practices like semantic chunking and privacy controls • Orchestrate multimodal pipelines • • using scalable frameworks (e.g., Apache Spark, PySpark) for automated ETL/ELT workflows appropriate for unstructured media • Implement embeddings drives—map media content to vector representations using embedding models, and integrate with vector stores (AWS KnowledgeBase/Elastic/Mongo Atlas) to support RAG architectures • • Required Qualifications: • • • 10+ years of experience in AI/ML, with 3+ years in applied GenAI or LLM-based solutions. • Deep expertise in prompt engineering, fine-tuning, RAG, GraphRAG, vector databases (e.g., AWS KnowledgeBase / Elastic), and multi-modal models. • Proven experience with cloud-native AI development (AWS SageMaker, Bedrock, MLFlow on EKS). • Strong programming skills in Python and ML libraries (Transformers, LangChain, etc.). • Deep understanding of Gen AI system patterns and architectural best practices, Evaluation Frameworks • Demonstrated ability to work in cross-functional agile teams.