Engineering Manager AI

Webellian is a well-established Digital Transformation and IT consulting company committed to creating a positive impact for clients. They serve diverse sectors such as insurance, banking, healthcare, retail, and manufacturing. The team consists of engineers and senior advisors working across industries to accelerate and realize client vision and strategy.

• 8+ years of engineering experience, including 3+ years people-management leading applied AI, ML, or conversational AI teams.
• Managed 6–15 engineers across at least two specializations (e.g., ML engineering and conversational/voice AI).
• Strong technical literacy in LLM-based agent systems, prompt engineering, agent design, voice/conversational AI platforms, production deployment on Kubernetes, API design, observability, and cost control.
• Experienced with embedded-specialist delivery models placing AI specialists in product teams.
• Experience running structured hiring at scale for AI roles with rubric-based interviews and onboarding.
• Skilled in formal performance management, career frameworks, and compensation calibration.
• Comfortable with AI governance, ethics review processes, and compliance in regulated industries.
• Ability to coordinate across organizational boundaries including peer managers, governance stakeholders, engineering teams, and external vendors.

Nice to have:

• Experience leading distributed AI delivery teams.
• Background in voice or conversational AI at production scale (e.g., contact-centre automation).
• Experience operating embedded-specialist models alongside central teams.
• Experience with AI governance frameworks like model cards and bias audits.
• Experience in insurance, financial services, or regulated industries with compliance and audit practices.

Key Responsibilities:

• Manage and grow AI teams: hiring, onboarding, performance management, career development, and 1:1s for ML/AI engineers, computational linguists, conversational AI engineers, and AI specialists.
• Balance engineering capacity across agent-based and voice/conversational AI capabilities according to product and domain needs.
• Coordinate embedded-AI-specialist delivery models and performance review with backend engineering leadership.
• Coach and develop technical leads on prompt engineering, agent design, NLU/NLP, and voice-flow design.
• Collaborate with AI governance stakeholders on compliance reviews regarding model choices, data handling, bias, and regulations.
• Coordinate change windows for AI service rollouts and integrate releases into operational change processes.
• Help balance AI roadmaps across agent and voice/conversational AI investments.
• Coordinate with backend and frontend engineering leadership on AI service consumption and AI-driven UX patterns.
• Work with delivery leads on ceremonies, cross-team planning, and delivery flow improvements.
• Run hiring processes and design interview rubrics aligned with the applied AI delivery model.