AI Lead

Role & responsibilities

Preferred candidate profile

b Description

ey Responsibilities

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:

  Masters or Ph.D. in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.