AI/ML Engineer - Python/Deep Learning

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 : - 24 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. (ref:hirist.tech)