AI Engineer

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.