Job Description

1. Strategic Leadership & Initiative Ownership Own the end-to-end lifecycle for new AI/ML initiatives, from initial business problem identification through architectural design, development, and eventual deployment. Define and execute the AI technology roadmap, constantly evaluating emerging technologies (e.g., LLMs, GenAI, Edge AI) and determining their strategic fit within the organization. Mentor and provide technical leadership to junior and mid-level Data Scientists and ML Engineers, fostering a culture of technical excellence and rapid experimentation. Serve as the principal technical authority for AI initiatives, communicating vision and progress to executive stakeholders. 2. Proof-of-Concept (POC) Development Lead the rapid prototyping and execution of AI POCs to validate technical feasibility and estimate business impact for new use cases. Design and implement complex ML architectures, including deep learning networks, reinforced learning models, and advanced statistical models, ensuring optimal performance and scalability. Establish clear metrics for POC success and failure, facilitating quick decision-making on whether to move from experimentation to full product development. 3. MLOps and Production Readiness Collaborate closely with DevOps and ML Engineering teams to define and implement best practices for MLOps, ensuring seamless integration of models into the production environment. Architect and oversee the deployment of scalable, high-availability, and low-latency inference services. Ensure all AI development adheres to strict governance, compliance, and ethical AI standards. Required Qualifications Experience: 10+ years of progressive experience in Data Science, Machine Learning, or Applied AI research, with a minimum of 3 years in a leadership or principal role. Technical Depth: Expert proficiency in Python and ML frameworks (PyTorch, TensorFlow). Deep knowledge of statistical modeling, machine learning fundamentals, and distributed computing. Ownership Track Record: Demonstrated history of successfully taking ownership of complex, ambiguous AI projects and driving them from initial concept/POC to stable production release. Architecture: Strong understanding of Microservices architecture and experience designing cloud-native ML solutions (AWS, GCP, or Azure). Communication: Exceptional ability to distill complex technical and mathematical concepts into clear business implications for executive leadership. Education: Master’s or Ph.D. in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.