Job Summary

Key Responsibilities

1. Technical Vision & Research Strategy

• Set the Technical Roadmap: Define the multi-year research and engineering roadmap for the AI platform, prioritizing projects that solve critical, high-complexity technical challenges (e.g., model efficiency, interpretability, real-time inference).
• Deep Learning & Modeling: Lead the team in designing, implementing, and optimizing advanced deep learning, generative AI, and reinforcement learning models from scratch, pushing the state-of-the-art for our domain.
• Academic Translation: Monitor academic research and industry trends to identify and quickly prototype cutting-edge techniques suitable for production deployment.

2. MLOps & Production Engineering

• MLOps Excellence: Establish and enforce best practices for Machine Learning Operations (MLOps), ensuring automation, reproducibility, version control, and CI/CD for all models.
• Architecture Review: Personally review and approve the technical architecture for all deployed AI systems, ensuring they meet strict criteria for scalability, low latency, and fault tolerance.
• Resource Optimization: Drive research into optimizing computational costs for large models, including strategies for model compression, quantization, and efficient hardware utilization.
  
  

3. Team Leadership & Technical Mentorship

• Lead Technical Talent: Recruit, mentor, and manage a high-performing team of Applied AI Scientists, Machine Learning Engineers, and Researchers.
• Culture of Rigor: Foster a technically demanding and research-driven culture, encouraging publication, patent filing, and open-source contributions.
• Code Quality: Ensure all core AI codebases adhere to the highest standards of quality, documentation, and maintainability.

Skill Requirements

Required Qualifications

• Hands-on experience in Machine Learning, Deep Learning, or AI Research, with a focus on building and deploying complex models and technical leadership experience managing a team of Data Scientists and ML Engineers.
• Expert-level proficiency in core ML frameworks (PyTorch, TensorFlow) and data science languages (Python/R).
• Experience building a commercial practice or product focused on Generative AI and Large Language Models (LLMs).
• Demonstrated expertise in at least two major AI domains (e.g., Deep Learning, NLP, Computer Vision).
• Proven track record of success in a client-facing or internal product-focused role, directly translating technical output into commercial results.
• Deep practical knowledge of MLOps principles and experience with cloud-native ML services (Google Cloud Vertex AI, SageMaker, Azure ML).
• Ph.D. or Master’s degree in Computer Science, Machine Learning, or a highly quantitative field, OR equivalent demonstrated technical leadership experience.
  
  

Technical / Functional Skills – Preferred Qualifications

• A strong portfolio of research publications (ICML, NeurIPS, KDD, etc.) or patents related to applied AI.
• Extensive experience with distributed computing frameworks (Spark, Ray) for large-scale model training and inference.
• Proven ability to manage large-scale, complex AI initiatives end-to-end.

Other Requirements