Head Of AI Engineering Injaz - Artificial Intelligence MIT

The Head of AI Engineering leads the design, development, and operationalization of enterprise-scale AI solutions, ensuring alignment with business strategy, regulatory requirements, and technology standards. This role owns the AI engineering roadmap, drives platform modernization, and establishes best practices for building, deploying, and governing AI models and agentic systems. The position partners with Data, Architecture, Security, and Business teams to deliver scalable, secure, and cost-efficient AI capabilities across the organization.

Strategic Leadership

• Define and execute the AI engineering strategy, ensuring alignment with enterprise AI vision and business priorities.
• Build and lead a high-performing AI engineering organization, including talent acquisition, upskilling, and succession planning.
• Drive adoption of responsible AI principles and ensure compliance with internal governance and external regulations.
  
  

Architecture & Platform Enablement

• Own the design and evolution of the enterprise AI platform, including MLOps, and agentic AI frameworks and platform.
• Establish standards for model lifecycle management, observability, and performance optimization.
• Integrate AI services with enterprise data platforms, APIs, and cloud-native architectures.
  
  

Delivery & Innovation

• Oversee delivery of high-impact AI solutions, including Copilot experiences, agentic workers, and corporate virtual assistants.
• Partner with product and business teams to prioritize use cases and ensure measurable business value.
• Foster innovation through sandbox environments, vendor partnerships, and collaboration with universities.
  
  

Governance & Risk Management

• Implement AI governance frameworks covering model risk, explainability, and bias mitigation.
• Ensure adherence to security, privacy, and regulatory standards (e.g., ISG, AI policy, model governance).
• Define cost governance for AI workloads, including GPU utilization and cloud resource optimization.
  
  

Change Management & Adoption

• Drive enterprise-wide AI literacy programs and change management initiatives.
• Establish communities of practice and knowledge-sharing forums to scale AI adoption.
  
  

Education: Bachelor’s or Master’s in Computer Science, AI/ML, or related field

Experience: 12+ years in technology leadership with at least 6 years in AI/ML engineering; proven track record of building and scaling AI platforms and teams.

Technical Expertise

• AI/ML frameworks, MLOps pipelines, containerization (Docker/Kubernetes).
• Cloud platforms (Azure preferred), GPU infrastructure, and distributed computing.
• Familiarity with Copilot/GenAI ecosystems, LLM fine-tuning, and prompt engineering.
  
  

Governance & Compliance: Strong understanding of AI risk management, regulatory compliance, and responsible AI practices.

Leadership Skills: Strategic thinking, stakeholder management, and ability to influence at executive level.

---