Data Scientist- Gen AI

Responsibilities

Design and implement advanced solutions utilizing Large Language Models (LLMs); Demonstrate self-driven initiative by taking ownership and creating end-to-end solutions; Conduct research and stay informed about the latest developments in generative AI and LLMs; Develop and maintain code libraries, tools, and frameworks to support generative AI development; Participate in code reviews and contribute to maintaining high code quality standards; Engage in the entire software development lifecycle, from design and testing to deployment and maintenance; Collaborate closely with cross-functional teams to align messaging, contribute to roadmaps, and integrate software into different repositories for core system compatibility; Possess strong analytical and problem-solving skills; Demonstrate excellent communication skills and the ability to work effectively in a team environment

Requirements

Natural Language Processing (NLP): Hands-on experience in use case classification, topic modeling, Q&A and chatbots, search, Document AI, summarization, and content generation; Computer Vision and Audio: Hands-on experience in image classification, object detection, segmentation, image generation, audio, and video analysis; Generative AI: Proficiency with SaaS LLMs, including Lang chain, llama index, vector databases, Prompt engineering (COT, TOT, ReAct, agents), Experience with Azure OpenAI, Google Vertex AI, AWS Bedrock for text/audio/image/video modalities; Familiarity with Open-source LLMs, including tools like TensorFlow/Pytorch and huggingface, Techniques such as quantization, LLM finetuning using PEFT, RLHF, data annotation workflow, and GPU utilization; Cloud: Hands-on experience with cloud platforms such as Azure, AWS, and GCP; Application Development: Proficiency in Python, Docker, FastAPI/Django/Flask, and Git; Strong analytical and problem-solving skills; Excellent communication skills and the ability to work effectively in a team environment

Nice to have

Cloud certification is preferred