Generative AI Engineer

As a Data Scientist, you will be responsible for fine-tuning and adapting open-source Large Language Models (LLMs) such as LLaMA 4, Mistral, and Bert using NVIDIA GPU tools. You will build AI agents utilizing frameworks like LangChain, LangGraph, or AutoGen with structured workflows including memory management, tools utilization, and retries. Implementing hybrid LLM solutions using OpenAI/Claude APIs and open-source models will be part of your key tasks. Additionally, you will be developing APIs using FastAPI, and containerizing applications with Docker. Your role will also involve deploying, monitoring, and scaling AI solutions on cloud providers like AWS, Azure, or similar platforms. Collaborating with senior engineers to optimize the performance and reliability of deployed systems will also be essential. The requirements for this role include hands-on experience with LLM fine-tuning and NVIDIA GPU toolkits, specifically CUDA. Familiarity with LangChain or similar agent frameworks is necessary. Experience in developing APIs with FastAPI and deploying them via Docker is also a requirement. Proficiency in using OpenAI/Anthropic APIs and building basic RAG pipelines is expected. A solid foundation in Python programming, cloud deployment on platforms like AWS/Azure, and working with vector databases such as FAISS and Pinecone is essential. Nice-to-have skills for this position include exposure to tools like LangServe and Semantic Kernel, familiarity with CI/CD pipelines, and monitoring tools like GitHub Actions and Prometheus. Previous contribution to open-source AI/ML projects will be considered advantageous. In addition to technical skills, soft skills are equally important for this role. Strong communication skills, both verbal and written, are essential. Excellent problem-solving and debugging skills are required. Being self-motivated with the ability to work independently as well as in a team setting is crucial. Comfort in working with stakeholders across different time zones will be beneficial for successful performance in this role.,