Jinal Shah

Background

About Jinal Shah

Jinal is qualified Data Scientist and PHD Scholar, Leads AI/Data Scientist's team focused on Advanced Gen.AI CoEs such as Domain Adapted LLMs, Decision Intelligence, Forecasting/Simulations and AI Observability. Specialization: LLM/SLM Fine-Tuning, RAG, Prompt Engineering, Cognitive Search, LLM Guardrails, LLM selection and benchmarking, Discriminative AI vs Gen. AI, LLM Build vs Buy strategy, ML Model building and tuning, AI Observability for agents and threat intelligenceMajor Solution Frameworks Developed: Language Model Domain Adaptation, Agentic AI Observability, Topic modelling and threat intelligence on AI observability, Forecasting and simulations for prediction use cases, Knowledge synthesis, Decision Intelligence for finance and investmentsResponsible for;1. Front facing customer to showcase transformational benefits using Generative and Discriminative AI, Conversational AI, NLP & Data science2. Driving Gen. AI/AI Solutions, Design, Architecture, Asset & Demo driven approach to foster better engagement with customer3. Build vs Buy strategy consulting with Business case analysis4. RFP/RFI and Deeper Engagement with customers thru Innovation days & proactive proposals5. Early mover to create Enterprise AI adoption, consulting, architecture, platform collateral creation6. Crawl, walk & run strategy. Result driven research, Quick EDA/POC/POT for AI led projects and productionize AI models7. AI Product/Platform evaluation, benchmarking & considerations for proposalWhitepapers/PoVs published:1. AI Observability for Agents, Threat Intelligence in Agentic AI, Observability for Business beyond LLM Tokens2. Domain Adapted LLM for Finance, Investments and Gaurdrails3. Conversational Experience Analytics4. Augment Search with Recommendation Engine5. LLM Hallucination management approaches | LLM Guardrails | RAI in Practice6. LLM Selection and Benchmarking Approach7. Enterprise Gen. AI Adoption strategy8. Knowledge synthesis on structured and unstructured data9. Agentic AI