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AI Engineering Manager

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Job Description - 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:

Team Leadership & Development
· 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

Strategic Delivery & Roadmap Ownership
· 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

Stakeholder Management & Governance
· 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

Quality & Performance Management
· 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

Skills & Requirements:

· 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.

Leadership Excellence:
Proven track record in people management and delivery leadership
AI Project Experience:
Hands-on exposure to AI initiatives including GenAI, voice bots, AI agents, and similar technologies
Technical Foundation:
Solid understanding of software engineering fundamentals and principles
Business Acumen:
ROI-focused mindset for evaluating and delivering AI solutions
Cloud & AI Expertise:
Working knowledge of cloud platforms and familiarity with AI tools, frameworks, and concepts
including LLMs, RAG, prompt engineering, and Python programming





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