Applied Science Manager - AI/ML

Key Responsibilities

• Lead and mentor a team of scientists and engineers to deliver high-quality ML/AI solutions.
• Own the full ML lifecycle from data exploration and feature engineering to model deployment and monitoring.
• Architect and implement models for recommendation systems, classification, forecasting, and generative AI use-cases.
• Collaborate cross-functionally with product, engineering, and MLOps to translate business needs into scalable ML solutions.
• Leverage LLMs and Retrieval-Augmented Generation (RAG) to build intelligent customer experiences.
• Champion model performance, interpretability, and reproducibility across varied domains.
• Contribute to reusable AI/ML tooling, pipelines, and best practices to improve development velocity
  

Basic Qualifications

• Master’s or PhD in Computer Science, Machine Learning, Statistics, or related field
• 5+ years of dedicated hands-on experience developing and deploying ML models at scale
• 2+ years of experience managing or mentoring data scientists or ML engineers
• Proficiency in Python and familiarity with languages like Java
• Solid foundation in ML techniques including regression, classification, deep learning, and collaborative filtering
• Experience with model deployment tools (e.g., FastAPI, Docker) and cloud ML platforms (AWS/GCP/Azure)
• Strong communication skills; ability to present complex ideas to technical and non-technical stakeholders
  

Preferred Qualifications

• Deep expertise in GenAI, NLP, time-series forecasting, or recommender systems
• Experience with LLM frameworks (e.g., OpenAI APIs, LangChain, Hugging Face Transformers)
• Proven success in deploying production-grade ML systems and managing MLOps workflows
• Track record of thought leadership via publications, patents, or internal innovations
• Ability to thrive in fast-paced, ambiguous environments with high autonomy