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 AI/ML/GenAI solutions using AWS 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 Agentic / 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 AWS. Proficient in deploying and monitoring ML solutions using AWS 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

• A Master's degree in Computer Science or Engineering.
• Minimum of 14 years of IT experience.
• At 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.
• Implemented at least one use case using Large Language Models (LLMs).
• Experience in prompt engineering and fine-tuning LLMs using techniques like LoRA or PEFT.
• Implemented a use case using RAG architecture.
• Experience with a Multi-agent framework is a strong plus.