ML Engineer / Data Scientist

Job Overview: Proven hands-on experience in Python programming , with expertise in popular AI/ML frameworks such as TensorFlow, PyTorch, scikit-learn, LangChain , and LlamaIndex . Strong background in building and implementing machine learning models . Hands-on experience in developing I/ML/GenAI solutions using WS services such as SageMaker . Experience with search algorithms, indexing techniques, summarization , and retrieval models for effective information retrieval tasks. Practical experience with RAG (Retrieval-Augmented Generation) architecture and its applications in Natural Language Processing (NLP) . Good exposure to gentic / Multi-agent frameworks . End-to-end experience in developing machine learning and deep learning solutions , including predictive modeling, applied machine learning , and natural language processing . Expertise in data engineering , including preprocessing and cleaning large datasets using Python, PySpark , and tools like Pandas and NumPy . Proficient in techniques such as data normalization, feature engineering , and synthetic data generation . Solid understanding of cloud computing principles and experience in deploying, scaling , and monitoring AI/ML/GenAI solutions on platforms like WS . Proficient in deploying and monitoring ML solutions using WS Lambda, API Gateway , and ECS , and tracking performance using CloudWatch . Experience with Docker and containerization technologies. Strong communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders , and to collaborate effectively with cross-functional teams . Must-Have Qualifications: Master's degree in Computer Science or Engineering . Minimum of 14 years of IT experience . t least 7 years of experience as a Machine Learning Engineer or Data Scientist . Hands-on experience using Python and APIs such as Flask, Django , or FastAPI . Practical experience with tools such as LangChain, LlamaIndex , and Streamlit . Experience working with semi-structured and unstructured data . Must have implemented at least one use case using Large Language Models (LLMs) . Must have experience in prompt engineering and fine-tuning LLMs using techniques like LoRA or PEFT . Must have implemented a use case using RAG architecture . Experience with a Multi-agent framework is a strong plus.