• Lead the design, development, testing, and deployment of AI/ML models and solutions. • Define AI/ML strategies and technical roadmaps aligned with business objectives. • Build and optimize machine learning and deep learning models for real-world applications. • Oversee data preprocessing, feature engineering, model training, validation, and evaluation. • Work with algorithms for classification, regression, clustering, and predictive analytics. • Explore and implement Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) solutions. • Develop scalable AI pipelines and production-ready ML applications. • Implement MLOps practices for model deployment, monitoring, versioning, and retraining. • Collaborate with data engineers, software developers, product managers, and business stakeholders. • Mentor and guide AI/ML engineers and ensure high-quality technical delivery. • Monitor model performance, accuracy, scalability, and reliability. • Ensure data privacy, model security, responsible AI practices, and compliance. • Evaluate emerging AI technologies and recommend suitable tools and frameworks. • Communicate project progress, technical insights, and business impact to stakeholders. Requirements • Strong programming skills in Python. • Expertise in machine learning libraries such as Scikit-learn, TensorFlow, and PyTorch. • Strong understanding of statistics, probability, linear algebra, and optimization. • Experience with data processing and analysis using Pandas, NumPy, and SQL. • Knowledge of deep learning, NLP, and computer vision. • Experience with Generative AI, LLMs, prompt engineering, and RAG architectures. • Familiarity with frameworks such as LangChain or LlamaIndex. • Experience with model deployment using FastAPI, Flask, or REST APIs. • Knowledge of MLOps tools, Docker, Kubernetes, and CI/CD pipelines. • Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud. • Strong leadership, problem-solving, communication, and project management skills.