AI Engineering Manager

Lead and scale a high-performing AI engineering team while driving end-to-end delivery of AI solutions from ideation through production deployment in the telecommunications sector.

Responsibilities:

· Build, manage, and mentor a team of AI Engineers and researchers through hiring, onboarding, and regular 1:1s

· Foster technical excellence by documenting and championing engineering best practices

· Conduct daily/weekly/monthly delivery ceremonies including stand-ups and sprint planning

· Co-own and execute the AI roadmap alongside the AI Solution Architect

· Manage the AI engineering backlog, prioritizing initiatives in collaboration with the Solution Architect

· Translate business requirements into actionable AI backlogs with product managers

· Own complete AI delivery lifecycle from concept to production deployment

· Coordinate across organizational stakeholders including Data Governance, Security, and country business teams

· Manage delivery expectations by clearly defining experiments, rollouts, and solution iterations

· Ensure compliance with internal and external governance frameworks and regulations

· Advocate for AI trends and technological advancements relevant to telecommunications

· Maintain high standards in AI delivery and operations

· Track team progress and solution performance using relevant KPIs and metrics · Adhere to AI development and delivery engineering lifecycle standards

· Hands-on coding skills and active involvement in AI development

· Practical experience in AI operations and MLOps

· Knowledge of digital and data technologies including web/mobile applications and

DataLake architectures.

Proven track record in people management and delivery leadership

Hands-on exposure to AI initiatives including GenAI, voice bots, AI agents, and similar technologies

Solid understanding of software engineering fundamentals and principles

ROI-focused mindset for evaluating and delivering AI solutions

Working knowledge of cloud platforms and familiarity with AI tools, frameworks, and concepts

including LLMs, RAG, prompt engineering, and Python programming