About the role
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.