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.