Business: Data and Architecture Office, Data Analytics Office

Principal responsibilities

• Strong understanding of LLM architectures and expertise in fine tuning pre trained models on domain specific data.
• Experience with RAG(Retrieval Augmented Generation), Prompt Engineering concepts and fundamentals (Vector DBs).
• Experience with containerization and orchestration technologies (Kubernetes, Docker).
• In depth knowledge of machine learning, deep learning, and NLP. Manage prioritization and technology work for building NLP, ML & AI solutions experience in experimenting and developing with LLM.
• Strong understanding of NLP techniques and framework such as BERT,GPT or Transformer models.
• Create and manage best practices for ML models integration, orchestration, and deployment, that will ensure secure including data versioning, ingress and model output egress, CI/CD pipelines for MLOps and LLMOps.
• Solid understanding of Machine learning concept and algorithm including supervised and unsupervised learning, model evaluation and deployment strategies in production environment.
• Follow AI best practices, ensuring fairness, transparency and accountability in AI model and system.

Requirements

• At least 8-10 years of experience in deploying end to end data science focused solutions. Should be strong technical background e.g.   B.Tech /M.Tech from top tier institutes.
• Expertise in training and fine tune LLMs using popular framework such as (TensorFlow, PyTorch or hugging face transformer) and deployment tools (e.g. Docker, Kubeflow)
• Good exposure in Python and strong knowledge in SQL, Pandas or Pyspark.
• Good to have understanding knowledge on GitHub, Confluence and JIRA
• Cloud Platforms knowledge have to: Azure /GCP
• Experience of senior stakeholder management and strong communication/presentation skills
• Understanding of concepts and principles within ESG. 
• Present technical solutions, capabilities, considerations, and features in business terms.