Solution Director

Bachelor of Technology/ Engineering, 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 MLOps (Machine Learning Operations), ensuring automation, reproducibility, version control, and continuous integration/continuous delivery (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."Qualifications, Skills and Competencies" 15+ years of hands-on experience in Machine Learning, Deep Learning, or AI Research, with a focus on building and deploying complex models. 5+ years of technical leadership experience managing a team of Data Scientists and ML Engineers. Expert-level proficiency in core ML frameworks (e.g., 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 (e.g., 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 man