Roles and Responsibilities

• Design, develop, and deploy scalable machine learning solutions for real-world applications.
• Collaborate with cross-functional teams to take research ideas to production, ensuring clean, modular, and sustainable code.
• Build and optimize high-performance, data-intensive applications using Python.
• Implement and fine-tune models using modern deep learning frameworks such as PyTorch, TensorFlow, or JAX.
• Apply domain expertise in one or more areas such as computer vision, large language models, recommender systems, or operations research.
• Utilize statistical techniques to evaluate model performance and solution feasibility.
• Monitor and maintain deployed ML models and pipelines, ensuring robustness and reliability in production.

Required Qualifications

• Strong proficiency in Python with a focus on high-performance, scalable applications.
• Hands-on experience with at least one modern deep learning framework (e.g., PyTorch, JAX, TensorFlow).
• Deep understanding and practical expertise in one or more ML domains: computer vision, NLP/LLMs, recommender systems, or operations research.
• Solid foundation in statistics and model evaluation methodologies.
• Proven track record of deploying and maintaining ML models in production environments.
• Commitment to writing clean, modular, and maintainable code.

Preferred Qualifications

• Experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow).
• Familiarity with cloud platforms (AWS, GCP, Azure).
• Background in research or academic collaboration is a plus.
• Contributions to open-source ML or deep learning projects.

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