Sr Technical Led

You have 10+ years of experience, primarily in Data Science, AI ML, GenAI, and NLP technologies. Additionally, you have at least 2+ years of experience in GenAI application development. You excel in problem-solving and developing technical solutions. Your expertise includes working with Agentic AI frameworks such as Lang Graph, Llama Index, Function calling, Chain of thoughts, MCP, Tools register, and intent-based routing, among others. In terms of AI frameworks, you are proficient in using paid options like OpenAI on Azure, Bedrock in AWS, as well as open-source options like LLMs, VLM, and SLM. You have a deep understanding of RAG, CAG, Knowledge Graph RAG, RAG Fusion, Agents, MCP, NLQ to SQL, and other related technologies. Your experience extends to handling large unstructured and structured data, including multiple file formats and data lakes. You are well-versed in multiple leading Vector DBs, chunking strategies, and Graph DBs like Neo4J. You have a proven track record of applying DAR for problem-solving and developing multi-agent systems using Agentic AI. Proficiency in Python programming is a must, and knowledge of other languages is also beneficial. You can efficiently develop Python code, especially using FastAPI. Your solid understanding of model development, model serving, training/re-training techniques in data sparse environments, and enforcing reusable component development sets you apart. Experience with the AWS/Azure ecosystem and developing solutions accordingly is a key requirement. You should also be familiar with Kubernetes and other serverless deployments. A valuable understanding of Prompt engineering techniques in developing Instruction-based LLMs is highly appreciated. Your role involves collaborating with SAs and cross-functional teams to identify business requirements and deliver solutions that meet customer needs. You are passionate about learning and staying updated with the latest advancements in generative AI and LLM. Your ability to articulate to business stakeholders on hallucination effects and model behavioral analysis techniques is crucial. Moreover, you have experience in developing Guardrails for LLMs using both open-source and cloud-native models. Collaboration with software engineers to deploy and optimize generative models in production environments is also part of your responsibilities, considering factors such as scalability and efficiency. You have 10+ years of experience, primarily in Data Science, AI ML, GenAI, and NLP technologies. Additionally, you have at least 2+ years of experience in GenAI application development. You excel in problem-solving and developing technical solutions. Your expertise includes working with Agentic AI frameworks such as Lang Graph, Llama Index, Function calling, Chain of thoughts, MCP, Tools register, and intent-based routing, among others. In terms of AI frameworks, you are proficient in using paid options like OpenAI on Azure, Bedrock in AWS, as well as open-source options like LLMs, VLM, and SLM. You have a deep understanding of RAG, CAG, Knowledge Graph RAG, RAG Fusion, Agents, MCP, NLQ to SQL, and other related technologies. Your experience extends to handling large unstructured and structured data, including multiple file formats and data lakes. You are well-versed in multiple leading Vector DBs, chunking strategies, and Graph DBs like Neo4J. You have a proven track record of applying DAR for problem-solving and developing multi-agent systems using Agentic AI. Proficiency in Python programming is a must, and knowledge of other languages is also beneficial. You can efficiently develop Python code, especially using FastAPI. Your solid understanding of model development, model serving, training/re-training techniques in data sparse environments, and enforcing reusable component development sets you apart. Experience with the AWS/Azure ecosystem and developing solutions accordingly is a key requirement. You should also be familiar with Kubernetes and other serverless deployments. A valuable understanding of Prompt engineering techniques in developing Instruction-based LLMs is highly appreciated. Your role involves collaborating with SAs and cross-functional teams to identify business requirements and deliver solutions that meet customer needs. You are passionate about learning and staying updated with the latest advancements in generative AI and LLM. Your ability to articulate to business stakeholders on hallucination effects and model behavioral analysis techniques is crucial. Moreover, you have experience in developing Guardrails for LLMs using both open-source and cloud-native models. Collaboration with software engineers to deploy and optimize generative models in production environments is also part of your responsibilities, considering factors such as scalability and efficiency.