Gen Ai Engineer

Client: TCS Payroll: Mbit Computraining Private Limited Note: Moved to direct TCS payroll after 3 months, subject to client confirmation and your performance Requirement: Gen Ai Engineer Exp: 5+ Years Location: Bangalore, Pune, Kochi, Chennai, coimbatore(WFO) Max Budget: 24LPA JD: AI/ML

Proficiency in AI/ML fields of Deep learning in Natural Language Programming (NLP) Strong understanding of NLP techniques and frameworks such as BERT, GPT or Transformer models. Deep learning in Computer Vision (CV) Basic understanding of computer vision techniques for image recognition or object detection Data engineering, including data curation, cleaning, and pre-processing large-scale datasets for generative AI applications Generative AI Concepts Knowledge of integrating backend with relevant APIs and libraries, such as Azure Open AI GPT models (Chat completion models and Embedders) and Hugging Face Transformers and LLMs, to leverage pre-trained models and enhance generative AI capabilities. Knowledge of vector databases and NoSQL databases like Elastic Search database/ Azure Cosmos DB to handle large-scale generative AI datasets and outputs. Prompt Engineering – Familiarity with prompt templates for Retrieval-Augmented Generation or Context-based Question-Answering. Knowledge of NLP concepts like similarity search algorithms and techniques to enable retrieval of relevant information to provide as context input to generative AI. Previous experience with Azure Cognitive services or Azure ML Studio. Large Language Model (LLM) finetuning for NLP tasks like Text Classification, Text Summarization, Question-Answering etc. Knowledge of LLM based frameworks within python like LangChain. Proficiency in programming languages such as Python, R, and other frameworks. Knowledge of Machine Learning Frameworks like Tensorflow, Pytorch, Scikit-learn, Numpy, Pandas Experience with cloud platforms such as Azure, or Google Cloud Platform (GCP) and deploying AI solutions in a cloud environment Understanding of AI evaluation metrics and methodologies to assess the quality, performance and relevance of generative AI outputs. Understanding of data privacy, security, and ethical considerations in AI applications.