Data Scientist/AI Engineer

An excellent opportunity for Data Scientist/AI Engineer to be part of Cognizant’s Intelligent Process Automation practice, It combines advisory services with deep vendor partnerships and integrated solutions to create and execute strategic roadmaps.

Key Responsibilities:

• Imagine new applications of generative AI to address business needs.
• Integrate Generative AI into existing applications and workflows.
• Collaborate with ML scientist and engineers to Research, design and develop cutting-edge generative AI algorithms to address real-world challenges
• Work across customer engagement to understand what adoption patterns for generative AI are working and rapidly share them across teams and leadership
• Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths for generative AI
• Create and deliver reusable technical assets that help to accelerate the adoption of generative AI on various platform
• Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder
• Provide customer and market feedback to Product and Engineering teams to help define product direction.

Key Skills and Experience :

• Proficient in Statistics, Machine learning and deep learning concepts.
• Skilled in python frameworks such as scikit-learn, scipy, numpy etc and DL libraries such as tensor flow, keras.
• Skilled in GenAI Projects such as text Summarization, chatbot creation using LLM models GPT4, Med-Palm, LLAMA etc.
• Skilled in fine tuning open source LLM models such as LLAMA2, google Gemma model to 1-bit LLM using LORA, Quantization and QLORA techniques.
• Skilled in RAG based Architecture using Langchain Framework & used Cohere model to fine tune and re rank the response of Genai based chatbots.
• Image classification using AI convolutional neural network model such as VGG 16, Resnet, Alex net, Darknet architectures using Computer vision domain.
• Object detection using various frameworks such as YOLO, TFOD, Detectron.
• Image classification, object detection, Tracking, Segmentation knowledge.
• Neural Network, BERT, Transformers, RAG, langchain, Prompt Engineering, Azure AI Search , Vector DB, Conversational AI, LLMs used: Azure open AI (Gpt4 turbo) ,LLAMA2 , Google Gemma, Cohere model, Azure Open AI Embedding Model