Head of Engineering (AI)

Job Title: Head of Engineering (AI)

 

Location:

[Location] (Opportunities for remote/hybrid/flexible work available)

 

Reports to:

CTO

 

Role Purpose

At [Company Name], we develop advanced Artificial Intelligence solutions to tackle complex problems for clients across [insert sectors]. With an office hub in [Location] and [onsite / remote or hybrid options available], we offer a dynamic setting where new ideas are highly encouraged. This Head of Engineering (AI) role guides our technical vision for AI initiatives, drives system architecture decisions, and leads talented engineering teams. You will be critical to shaping our AI roadmap and ensuring projects meet high standards of quality, scalability, and security.

 

Company Overview

[Company Name] is a pioneering firm in the [Industry] sector, known for translating cutting-edge research into practical AI applications. We value collaboration, ethical standards, and a strong focus on results. Our culture is built on mutual respect, continual learning, and transparency. We have received industry-wide recognition for our AI-focused projects, placing us at the forefront of innovation in our field. Our teams enjoy a supportive environment that encourages professional growth and engagement with emerging technologies.

 

Key Responsibilities

• Technical Direction & Strategy

  • Define the long-term AI engineering strategy, aligning with business goals and product milestones.

  • Champion best practices for Machine Learning and Deep Learning workflows, ensuring the development of secure and maintainable systems.

• Leadership & Team Management

  • Oversee multiple AI and software engineering teams, setting performance objectives and mentoring team members.

  • Recruit, train, and retain top-tier talent capable of designing and deploying complex AI solutions.

  • Promote a culture of accountability, open communication, and mutual respect.

• Architecture & System Design

  • Guide the design of scalable AI architectures, including data pipelines, model training infrastructure, and real-time inference systems.

  • Make key decisions on technology stacks and frameworks (e.g., TensorFlow, PyTorch, Kubernetes, Docker) to optimize performance, cost, and flexibility.

  • Ensure robust integration with existing products and platforms, including APIs and microservices.

• Research & Development

  • Collaborate with research teams to bridge experimental AI developments and real-world implementation.

  • Evaluate emerging tools and methodologies that could elevate company capabilities, and steer their strategic adoption.

• Cross-Functional Collaboration

  • Partner with Product Management, Data Science, and UX teams to deliver AI-driven features aligned with customer needs and market opportunities.

  • Communicate project status, timelines, and key deliverables to stakeholders at all levels, from engineers to executive leadership.

• MLOps & Deployment

  • Develop and enforce standardized CI/CD pipelines, containerization, and model versioning processes.

  • Implement robust observability measures—including monitoring, logging, and alerting—to maintain high operational standards.

• Quality & Compliance

  • Ensure adherence to data privacy regulations (e.g., GDPR) and internal governance protocols.

  • Oversee documentation, testing practices, and code reviews to deliver stable, maintainable products.

• Budget & Resource Allocation

  • Manage departmental budgets, project timelines, and resource planning. [quantify this information if possible]

  • Provide clear guidance on workload priorities, ensuring optimal utilization of both in-house and external resources.
     

 

Required Skills and Qualifications

• Educational Background

  • Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field; a Ph.D. is advantageous.

• AI and Machine Learning Expertise

  • Proven track record (e.g., 10+ years) in software engineering, with at least 5 years focused on AI/ML projects.

  • Proficient in machine learning frameworks (e.g., TensorFlow, PyTorch) and languages such as Python, C++, or Go.

  • Experienced in designing large-scale data processing pipelines, including ETL, feature engineering, and distributed computing.

• Cloud & Infrastructure

  • Hands-on knowledge of cloud services (AWS, Azure, or GCP), including container orchestration tools like Kubernetes.

  • Familiarity with Infrastructure as Code approaches (e.g., Terraform) and continuous deployment tools (e.g., Jenkins, GitLab CI).

  • Practical understanding of DevOps or MLOps principles for streamlined model deployment and lifecycle management.

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