Sr Engineer - Generative AI

Role & responsibilities • Ability to build small-scale GenAI applications, experiment with prompts, and integrate LLM APIs. • Strong knowledge of Python and ML frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers. • Ability to implement simple RAG-based solutions using vector stores and embedding models. • Strong skills in data cleaning, preparation, unstructured text processing, and dataset creation. • Exposure to fine-tuning or configuring pre-trained models for domain-specific tasks. • Exposure to using Git, Docker, notebooks, and basic MLOps workflows. • Understanding of LLM architectures, embeddings, prompt engineering, fine-tuning basics, and NLP concepts. • Ability to work effectively with Sr data scientists, Sr engineers, and domain experts/Technology Head, Client to deliver AI solutions. Essential Duties/Responsibilities • Build and deploy Generative AI models for text, image, and multimodal applications / API on cloud platform (AWS, Azure, GCP) or containerized environments (Docker) • Fine-tune or adapt Large Language Models (LLMs), diffusion models, or other generative architectures using domain-specific datasets. • Develop and test GenAI-powered microservices using frameworks like FastAPI or Flask. • Build pipelines for data preparation, model training, and validation with frameworks like PyTorch, TensorFlow, or Hugging Face • Develop APIs and back-end systems (FastAPI, Flask, Node.js) to serve model outputs efficiently. • Design and implement vector databases (FAISS, Chroma, Pinecone, Weaviate) to connect models with enterprise data. 7)Integrate GenAI workflows with CI/CD and MLOps pipelines for automated deployment. • Build and optimize RAG pipelines to ensure factual, context-aware responses from LLMs. • Build proof-of-concepts (POCs) to test emerging GenAI use cases for business problems • Mentor Entry level engineers ,Interns & guide them about tools, frameworks, and deployment workflows.