AI/ML Engineer

Key Responsibilities Design, develop, and deploy machine learning and deep learning models for real-world business applications. Build and optimize AI-powered solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and chatbot frameworks. Develop NLP-based conversational AI applications, virtual assistants, and intelligent automation solutions. Build computer vision solutions for image classification, object detection, OCR, and related use cases. Fine-tune, evaluate, and optimize transformer-based models for performance and accuracy. Develop and deploy APIs using FastAPI, Flask, or similar frameworks. Integrate AI/ML models into production environments and ensure scalability and reliability. Work with cloud platforms (AWS, GCP, or Azure) for model training, deployment, and monitoring. Collaborate with product, engineering, and data teams to deliver end-to-end AI solutions. Stay updated with the latest advancements in Generative AI, LLMs, and machine learning technologies. Required Skills & Qualifications 3-7 years of hands-on experience in AI/ML engineering, machine learning, NLP, Generative AI, or computer vision. Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, and OpenAI APIs. Experience working with Large Language Models (LLMs) and building AI-powered chatbot applications. Hands-on experience with Retrieval-Augmented Generation (RAG) architectures and prompt engineering. Experience in computer vision using OpenCV, YOLO, or similar frameworks. Knowledge of machine learning algorithms, model evaluation, and optimization techniques. Experience building and consuming REST APIs using FastAPI or Flask. Familiarity with MLOps concepts, model deployment, monitoring, and version control. Experience with cloud platforms (AWS, GCP, or Azure). Knowledge of containerization tools such as Docker; exposure to Kubernetes is a plus. Strong analytical, problem-solving, and communication skills. Preferred Skills (Good to Have) Experience with vector databases such as FAISS, Pinecone, Weaviate, or ChromaDB. Exposure to AI orchestration frameworks such as LangChain, LlamaIndex, or LangGraph. Experience working with multi-modal AI models (text, image, audio). Knowledge of model serving and inference optimization techniques. Exposure to CI/CD pipelines and DevOps practices for AI applications. Understanding of AI governance, responsible AI, and security best practices.