Machine Learning & NLP Development Design, develop, and deploy Machine Learning and NLP solutions for business-critical applications. Build and optimize text classification, information extraction, sentiment analysis, entity recognition, summarization, and conversational AI systems. Develop and fine-tune Transformer-based models and LLM-powered applications. Evaluate model performance and implement improvements for accuracy, scalability, and efficiency. Generative AI & LLM Applications Develop GenAI applications using Large Language Models (LLMs). Design and implement Retrieval-Augmented Generation (RAG) pipelines. Build AI copilots, chatbots, virtual assistants, and intelligent search solutions. Apply prompt engineering techniques to optimize model outputs. Develop agentic AI workflows using modern AI orchestration frameworks.
Additional Responsibilities:
Research & Innovation Stay updated with advancements in NLP, Generative AI, LLMs, and Machine Learning. Evaluate emerging AI technologies and recommend adoption where appropriate. Conduct experiments and proof-of-concepts (POCs) to solve complex business problems. Leadership & Mentoring Mentor junior ML engineers and data scientists. Conduct code reviews, model reviews, and design reviews. Promote best practices in machine learning engineering and AI development. Collaborate with business stakeholders, product teams, and engineering leaders.
Technical and Professional Requirements:
Data Preparation & Feature Engineering Process and analyze large-scale structured and unstructured datasets. Perform text preprocessing, tokenization, feature extraction, and embedding generation. Build scalable data pipelines to support AI model training and inference. Model Deployment & MLOps Deploy, monitor, and maintain ML models in production environments. Implement model versioning, tracking, and serving using MLflow. Develop CI/CD pipelines for machine learning workflows. Monitor model drift, performance metrics, and system reliability.
