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.

• 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.
• Experience with vector databases such as FAISS, Pinecone, Weaviate, or ChromaDB. (Good to Have)
• Exposure to AI orchestration frameworks such as LangChain, LlamaIndex, or LangGraph. (Good to Have)
• Experience working with multi-modal AI models (text, image, audio). (Good to Have)
• Knowledge of model serving and inference optimization techniques. (Good to Have)
• Exposure to CI/CD pipelines and DevOps practices for AI applications. (Good to Have)
• Understanding of AI governance, responsible AI, and security best practices. (Good to Have)