Ai Ml Engineer

Roles & Responsibilities

• Develop, train, and deploy machine learning and deep learning models.
• Build predictive models for classification, regression, clustering, and recommendation problems.
• Develop NLP, computer vision, and Generative AI solutions based on business requirements.
• Work with LLMs, embeddings, RAG pipelines, and prompt engineering for GenAI applications.
• Deploy ML models through APIs, cloud platforms, or production applications.
• Monitor model performance, data quality, and model drift in production.
• Collaborate with data engineers, software developers, product teams, and business stakeholders.
• Maintain documentation, experiment tracking, version control, and reproducible ML workflows.
• Stay updated with emerging AI/ML techniques, frameworks, and tools.

Must Have Skills:

• Strong proficiency in Python for developing AI/ML applications and data processing workflows.
• Strong knowledge of Machine Learning algorithms for classification, regression, clustering, and prediction.
• Strong understanding of Deep Learning concepts, neural networks, CNNs, RNNs, and Transformers.
• Hands-on experience with NumPy, Pandas, Matplotlib, and Scikit-learn.
• Experience with PyTorch or TensorFlow for developing and training deep learning models.
• Strong knowledge of SQL for querying, transforming, and analyzing data.
• Experience with NLP, LLMs, Generative AI, prompt engineering, and RAG.
• Knowledge of Git and GitHub for version control and collaborative development.
• Experience with REST APIs and FastAPI/Flask for deploying ML models.
• Knowledge of Docker and containerization for application deployment.
• Understanding of AWS, Azure, or GCP cloud platforms.
• Knowledge of MLOps practices, including model deployment, monitoring, versioning, and CI/CD.