Job Description

investigate and fix bugs; create automated test scripts. - Maintain, operate, and monitor solutions - Lead the development of prototype designs for a product using a wide range of techniques - Demonstrate and document solutions by using flowcharts, diagrams, code comments, code snippets, and performance instruments. Guide others on the team in documenting their code. - Recommend software tools to management and architecture teams -Implement and maintain the infrastructure needed for end-to-end machine learning workflows including data collection, model training, and deployment in production environments. -Manage the lifecycle of large language models including training, evaluation, deployment, and monitoring of these models. -Evaluate the performance of various machine learning models using appropriate metrics and statistical tests. Make recommendations on which models to use based on their performance. -Fine-tune and enhance existing Retrieval Augmented Generation (RAG) and LLM models to improve their performance and adaptability. -Design and optimize prompts to effectively guide the behavior of language models. Understand and manage the flow of prompts in a conversation or task. -Provide guidelines and strategies for improving the accuracy of machine learning models. -Advise on how to optimize token usage to save costs and improve performance including strategies for efficient data preprocessing, model architecture design, and deployment. -Leverage Azure AI technologies for developing, deploying, and managing AI solutions with Azure Machine Learning and Cognitive Services Bachelors in engineering/ B.E/B. Tech and/or required equivalent MCA. Overall 12+ years' experience with proven track record of 5+ years in Solutioning and deploying end to end AI Net, JavaScript, Angular or React, SQL Server, Azure deployment services, Kubernetes, GitHub , Azure Cognitive Services , GPT models, added advantage with GitHub Copilot