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.