Architect

Long Description

Must have 12 - 15 years of experience working in Data science, AI ML, GenAI and NLP technologies.
Experts in guiding customer, solution design using architecture principles Solid understanding of Model development, model serving, training/re-training techniques in a data sparse environment. Must have experience with Agentic AI frameworks – LangGraph, LlamaIndex, MCP etc. Expert in using paid (OpenAI on Azure, Bedrock in AWS) and open source LLMs, VLM, SLM etc Expert in RAG, CAG, Knowledge Graph RAG, RAG Fusion, Agents, MCP etc. Experience in handling large unstructured (multiple file formats) and structured (data Lake) data DAR for applying best solution for problem statements Strong understanding of Agents development Experience with Python programming language in a must. Should know other languages too Ability to develop Python code as needed and train the Developers based on business needs.• Enforce reusable component development and sharing across solutions Experience with AWS / Azure ecosystem and development accordingly is a must. Kubernetes and other server less deployments Preferable candidate should possess Pharma R&D background Able to apply deep learning and generative modeling techniques to develop LLM solutions in the field of Artificial Intelligence. Utilize your extensive knowledge and expertise in machine learning (ML) with a focus on generative models, including but not limited to generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer-based architectures. Very good understanding of Prompt engineering techniques in developing Instruction based LLMs. Must be able to design, and implement state-of-the-art generative models for natural language processing (NLP) tasks such as text generation, text completion, language translation, and document summarization. Work with SAs and collaborate with cross-functional teams to identify business requirements and deliver solutions that meet the customer needs. Passionate to learn and stay updated with the latest advancements specially in generative AI and LLM. Nice to have -contributions to the research community through publications, presentations, and participation in relevant conferences or workshops. Evaluate and preprocess large-scale datasets, ensuring data quality and integrity, and develop data pipelines for training and evaluation of generative models. Ability to articulate to business stakeholders on the hallucination effects and various model behavioral analysis techniques followed. Exposure to developing Guardrails for LLMs both with open source and cloud native models. Collaborate with software engineers to deploy and optimize generative models in production environments, considering factors such as scalability, efficiency, and real-time performance. Nice to have- provide guidance to junior data scientists, sharing expertise and knowledge in generative AI and LLM, and contribute to the overall growth and success of the data science team. Handel other data scientists Expert in RDBMS database, Data Lake, DW, BI Experience on Marklogic / No SQL database Experience on Elastic search

Area(s) of responsibility