Senior Engineer/Lead/Architect - Machine Learning/Artificial Intelligence

You should have a minimum of 5 years of experience in AI/ML with at least 2+ years in NLP, LLMs, and Generative AI. Your expertise should include ML architecture design, end-to-end model development, and deployment in production systems. Proficiency in Python is essential, along with deep experience in ML libraries and frameworks like TensorFlow, PyTorch, Hugging Face, and LangChain. A sound knowledge of transformer models, embeddings, tokenization, and vector databases such as FAISS and Pinecone is required. Experience with cloud-native AI solutions on AWS, Azure, or GCP is preferred. Familiarity with MLOps, model versioning, containerization using Docker, and orchestration tools like Kubeflow and MLflow is a plus. Your responsibilities will include architecting and implementing end-to-end machine learning and Generative AI solutions for real-world applications. You will design, fine-tune, and deploy models using transformers, embeddings, tokenization, and LLMs for tasks like summarization, classification, question answering, and content generation. Developing and maintaining high-quality, production-grade ML code in Python using libraries such as TensorFlow, PyTorch, Hugging Face, and LangChain is crucial. Furthermore, you will be responsible for building and optimizing retrieval-augmented generation (RAG) pipelines by integrating LLMs with structured and unstructured data. Working with vector databases like FAISS and Pinecone to manage semantic search and context retrieval efficiently will be part of your role. Utilizing cloud-native AI services for model training, deployment, and scaling on platforms like AWS, GCP, and Azure is expected. Implementing MLOps best practices, including model versioning, containerization using Docker, orchestration with tools like Kubeflow and MLflow, and following CI/CD procedures are also key responsibilities. Strong problem-solving skills, architectural thinking, and the ability to lead complex AI initiatives, along with excellent communication, stakeholder management, and technical leadership capabilities, are essential for this role.,