Data Scientist - Artificial Intelligence/Machine Learning

As a Data Scientist/AI/ML Engineer, your role will involve working with cutting-edge technologies such as GenAI, Agentic AI, Multi-agentic systems, RAG, MCP, and Observability for Agentic AI. You should have hands-on experience with Large Language Models (LLMs), fine-tuning, prompt engineering, and familiarity with agentic AI frameworks. Additionally, clinical data management experience, including familiarity with SDTM, ADaM, and TLF, will be a strong advantage. Your key responsibilities will include: - Architecting and implementing end-to-end machine learning pipelines to automate data processing and model training for improved operational efficiency. - Developing and fine-tuning deep learning models, such as LLMs and NLP frameworks, to enhance the functionality of client-facing applications. - Collaborating with cross-functional engineering teams to integrate AI models into existing production environments, ensuring high availability and performance. - Conducting rigorous testing and validation of algorithms to ensure model accuracy, fairness, and compliance with industry standards. - Optimizing existing AI infrastructure to reduce latency and improve the scalability of data-intensive applications for global clients. The must-have skills for this role include: - Generative AI (GenAI) - Agentic AI (Production-grade systems) - Multi-Agent Systems - RAG (Retrieval-Augmented Generation) - MCP (Model Context Protocol) - Agentic AI Observability - Agentic AI Evaluation Frameworks - Large Language Models (LLMs) - LLM Fine-Tuning - Prompt Engineering - Agentic AI Frameworks (LangChain, CrewAI, AutoGen, Semantic Kernel, etc.) Good to have skills include experience in Clinical Data Management with SDTM, ADaM, and TLF. As a Data Scientist/AI/ML Engineer, your role will involve working with cutting-edge technologies such as GenAI, Agentic AI, Multi-agentic systems, RAG, MCP, and Observability for Agentic AI. You should have hands-on experience with Large Language Models (LLMs), fine-tuning, prompt engineering, and familiarity with agentic AI frameworks. Additionally, clinical data management experience, including familiarity with SDTM, ADaM, and TLF, will be a strong advantage. Your key responsibilities will include: - Architecting and implementing end-to-end machine learning pipelines to automate data processing and model training for improved operational efficiency. - Developing and fine-tuning deep learning models, such as LLMs and NLP frameworks, to enhance the functionality of client-facing applications. - Collaborating with cross-functional engineering teams to integrate AI models into existing production environments, ensuring high availability and performance. - Conducting rigorous testing and validation of algorithms to ensure model accuracy, fairness, and compliance with industry standards. - Optimizing existing AI infrastructure to reduce latency and improve the scalability of data-intensive applications for global clients. The must-have skills for this role include: - Generative AI (GenAI) - Agentic AI (Production-grade systems) - Multi-Agent Systems - RAG (Retrieval-Augmented Generation) - MCP (Model Context Protocol) - Agentic AI Observability - Agentic AI Evaluation Frameworks - Large Language Models (LLMs) - LLM Fine-Tuning - Prompt Engineering - Agentic AI Frameworks (LangChain, CrewAI, AutoGen, Semantic Kernel, etc.) Good to have skills include experience in Clinical Data Management with SDTM, ADaM, and TLF.