AI Engineering Manager

About the role

We are looking for a hands‑on AI Engineering Manager to lead a high‑performance team of AI and machine learning engineers. The role combines deep technical expertise with strategic leadership, guiding the design, development and deployment of innovative AI‑powered products and platforms.

Key responsibilities

• Act as the senior technical authority for the AI engineering team, shaping architecture, design and implementation of scalable, reliable and secure AI systems.
• Spend a significant portion of time on hands‑on development, including coding, code reviews and architecting complex solutions.
• Modernize legacy systems by integrating AI capabilities such as retrieval‑augmented generation, code generation and multi‑agent workflows.
• Oversee the full AI project lifecycle from experimentation to production deployment and continuous optimization.
• Establish and enforce best practices for the software development lifecycle, including agile methods, CI/CD pipelines and automated testing.
• Collaborate with executive leadership and product management to define AI strategy and roadmap aligned with business goals.
• Make build‑vs‑buy decisions for AI tools and platforms, manage budgets, resources and vendor relationships.
• Lead, mentor and grow a team of AI engineers, fostering a culture of innovation, collaboration and continuous learning.
• Drive hiring, onboarding and career development of engineering talent.

Required profile

• 13–15 years of software engineering experience, with a strong focus on AI, machine learning or data science.
• 5–8 years of leadership experience managing high‑performing engineering teams.
• Bachelor’s or Master’s degree in Computer Science, Engineering or a related field (PhD a plus).
• Proven track record delivering large‑scale, complex AI projects from concept to production.
• Excellent problem‑solving, communication and interpersonal skills.

Required skills

• Python, Java, C++
• Machine learning, deep learning, natural language processing, generative AI
• Design and operation of scalable AI infrastructure and MLOps/LLMOps pipelines on AWS, Azure or GCP
• System design, microservices architecture and API development
• Agile methodologies, CI/CD, automated testing