Seeking a Senior Data Science Lead specializing in Generative AI (GenAI) development. This role requires extensive experience in AI, ML, GenAI, and NLP technologies, with a significant focus on building GenAI applications. You will be instrumental in crafting innovative solutions leveraging cutting-edge AI frameworks and large language models.

We are looking for experts in problem-solving and technical solution development. The ideal candidate will possess a deep understanding of Agentic AI frameworks, including LangGraph and LlamaIndex, and proficiency with both paid and open-source LLMs, VLMs, and SLMs.

Your responsibilities will include working with diverse datasets, implementing advanced RAG and CAG techniques, and developing robust agents. A strong grasp of Python, FastAPI, and cloud ecosystems like AWS and Azure is essential, as is experience with Kubernetes and serverless deployments. You will also be adept at prompt engineering and building guardrails for LLMs.

Design and develop cutting-edge Generative AI applications, focusing on GenAI and NLP technologies.

Implement advanced techniques such as RAG, CAG, Knowledge Graph RAG, RAG Fusion, and Agent development.

Leverage expertise in Agentic AI frameworks like LangGraph, LlamaIndex, MCP, and Tools Register.

Work with various LLMs, including OpenAI on Azure, AWS Bedrock, and open-source models.

Handle large volumes of unstructured and structured data, including data lakes.

Develop Python code efficiently using FastAPI and ensure code reusability.

Deploy and optimize generative models in production environments with software engineers, considering scalability and real-time performance.

Collaborate with Solutions Architects and cross-functional teams to translate business requirements into actionable technical solutions.

Stay abreast of the latest advancements in generative AI and LLMs, driving continuous learning and innovation.

A minimum of 8 years of experience in Data Science, AI/ML, GenAI, and NLP.

At least 2 years of direct experience in GenAI application development.

Expertise in problem-solving and developing sophisticated technical solutions.

Proven experience with Agentic AI frameworks such as LangGraph, LlamaIndex, MCP, Tools Register, and intent-based routing.

Proficiency with paid LLMs (OpenAI on Azure, AWS Bedrock) and open-source LLMs, VLMs, and SLMs.

Deep understanding of RAG, CAG, Knowledge Graph RAG, RAG Fusion, Agents, MCP, and NLQ to SQL.

Experience managing and processing large-scale unstructured and structured data, including Data Lakes.

Strong proficiency in Python programming, with experience in FastAPI for efficient code development.

Solid understanding of model development, serving, and training/re-training techniques, particularly in data-sparse scenarios.

Experience with the AWS/Azure ecosystems, including Kubernetes and serverless deployments.

Excellent command of prompt engineering techniques for instruction-based LLMs.

Experience developing guardrails for LLMs using both open-source and cloud-native models.

Familiarity with RDBMS, Data Lakes, PostgreSQL, MarkLogic, NoSQL databases, and Elasticsearch.

Immediate joiners are preferred.