Data Scientist Specialist

Technology Ventures is seeking a highly experienced Principal Gen AI Scientist to lead the design and development of cutting-edge AI Agents and Gen AI Applications. This role involves architecting scalable solutions, optimizing large language models, and collaborating with cross-functional teams to implement enterprise-grade Gen AI solutions.

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