GEN AI / LLM Scientist at Accenture

Responsibilities :

Intelligence and Insights (I& I) GenAI/LLM Scientist in our Talent and Organization: a typical day might include :

- Build data-driven GenAI-enabled solutions, including frameworks, products, and offering prototypes.

- Engage with clients to understand their challenges and recommend building, orchestrating, and running complex AI workflows and agents using large language models (LLMs).

- Hands-on experience in designing, deploying, and optimizing LLM-based applications using RAG architectures, LangChain, agentic frameworks, LangGraph, Bedrock Agents, etc.

- Identify, analyze, and interpret trends or patterns in complex data sets to develop business solutions by determining the best I& I solutions.

- Build strong relationships with clients and foster business growth with clear communication and stakeholder engagement.

- Define data-backed people strategies to support business transformational programs.

Requirements :

- MBA/Master's degree in appropriate areas like DS, AI/ML, etc.

- Self-learners with hands-on expertise in GenAI, Large Language Models (LLMs), Agentic AI, RAG framework, LangChain/LangGraph, vector DB, and FAISS, delivering real-world AI impact at scale.

- 4 to 6 years of experience in developing and deploying applications on cloud platforms using Python, along with a few years of experience in delivering complex AI workflows and agents using LLMs.

- Working knowledge of MLOps, including building and managing end-to-end ML pipelines, model deployment to production environments, safety guardrails, monitoring, and maintaining deployed models in Azure and AWS.

- Working knowledge of Natural Language Processing (NLP), including TensorFlow, BERT, Transformer architecture, and Hugging Face models.

- Good to have domain knowledge in people analytics and people management-related programs (workforce transformation, workforce M& A strategy, culture transformations, I& D strategy, etc. ).

- Data fluency, data analysis and storytelling, and working knowledge of statistical methodologies.

- Ability to perform in a non-structured and dynamic environment.