Engineering Manager AI

Webellian is a well-established Digital Transformation and IT consulting company committed to creating a positive impact for our clients. We strive to make a meaningful difference in diverse sectors such as insurance, banking, healthcare, retail, and manufacturing. Our passion for cutting-edge and disruptive technologies, as well as our shared values and strong principles, are what motivate us. We are a community of engineers and senior advisors who work with our clients across industries, playing a deep and meaningful role in accelerating and realizing their vision and strategy.

About the position

As an Engineering Manager within an Advanced Analytics / AI organization at a large enterprise, you will manage AI engineering teams building agent-based AI services and conversational/voice AI systems that integrate with digital products.

The remit covers two delivery surfaces: agent-based AI services consumed by domain teams via APIs and voice/conversational AI experiences for customer-facing and operational use cases.

You will own people management, performance, hiring, resourcing, and delivery leadership. Technical decisions remain with senior engineers and technical leads in each team. Your role is to build the people structure, capacity, and ways of working that enable engineers to ship applied AI into production.

You will partner with peer Engineering Managers on cross-team dependencies, with AI governance stakeholders on compliance and ethics reviews, and with senior AI engineering leadership on production change management.

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 embedded with delivery teams.
• Balance engineering capacity across agent-based and voice/conversational AI capabilities in response to demand from product and domain teams; coordinate with senior leadership on team sizing and skill mix.
• Coordinate an embedded-AI-specialist model with backend engineering leadership: agree how specialists join product delivery, how their performance is reviewed, and how their work is balanced between embedded delivery and central capability building.
• Coach and develop technical leads across the teams, supporting them as authorities on prompt engineering, agent design, NLU/NLP, and voice-flow design while maintaining consistent standards.
• Work with AI governance stakeholders on compliance reviews covering model choices, data handling, bias, and relevant compliance requirements.
• Coordinate change windows for AI service rollouts and integrate AI releases into broader operational change processes.
• Help balance AI roadmaps across agent and voice/conversational capabilities, ensuring investment is appropriately staged across individual and hybrid use cases.
• Coordinate with backend engineering leadership on how AI services are consumed in production.
• Coordinate with frontend engineering leadership on AI-driven UX patterns.
• Work with delivery leads on ceremonies, cross-team planning, and continuous improvement of delivery flow.
• Run hiring processes and design interview rubrics for ML/AI engineers, computational linguists, conversational AI engineers, and embedded AI specialists aligned with the team's applied-AI delivery model.

• 8+ years of engineering experience, including at least 3 years in a people-management role leading applied AI, ML, or conversational AI delivery teams.
• Track record managing approximately 6-15 engineers across at least two distinct teams or specialisations, such as ML engineering and conversational/voice AI.
• Strong technical literacy across the AI stack, sufficient to mentor and challenge technical teams without overriding their technical leads:
  • LLM-based agent systems: prompt engineering, agent design patterns, evaluation, and retrieval augmentation.
  • Voice and conversational AI platforms: NLU/NLP system design, speech-pipeline integration, and dialogue management.
  • Production deployment of AI services on Kubernetes, including API design, observability, latency, and cost control.
  • Embedded-specialist delivery models: placing specialists into product teams without losing central capability ownership.
• Experience running structured hiring at scale for AI roles, including rubric-based interviews, calibration, and onboarding programmes.
• Experience with formal performance-management cycles, career frameworks, and compensation calibration.
• Comfortable working with formal AI governance and ethics review processes, with familiarity with AI compliance requirements in regulated industries.
• Demonstrated ability to coordinate across organisational boundaries, including peer Engineering Managers, governance stakeholders, backend and frontend delivery teams, and external vendors.

• Leads through coaching rather than directing; technical authority remains with senior engineers and technical leads.
• Comfortable in agile, iterative delivery environments, with a clear bias toward unblocking teams rather than centralising decisions.
• Pragmatic about applied AI: focused on shipping AI capabilities into production for measurable business outcomes rather than research for its own sake.
• Clear communicator across global, cross-functional stakeholders; able to translate AI capability and reliability into business impact for non-technical audiences.
• Pragmatic adopter of AI-assisted developer tools and supports teams in integrating them into daily delivery.

• Experience leading distributed AI delivery teams.
• Background in voice or conversational AI at production scale, such as contact-centre automation or voice-first product experiences.
• Experience operating an embedded-specialist model alongside a central capability team.
• Experience with AI governance frameworks, including model cards, bias audits, and regulatory attestations.
• Experience in insurance or financial-services domains, including claims, policy, or payments.
• Experience in regulated industries such as insurance, finance, or healthcare, where compliance, audit, and access control are part of standard delivery practice.

• Contract under Polish law: B2B or Umowa o Pracę
• Benefits such as private medical care, group insurance, Multisport card
• There are English classes available
• Hybrid work (at least 1 day per quater on-site) in Warsaw (Mokotów)
• Opportunity to work with excellent professionals
• High standards of work and focus on the quality of code
• New technologies in use
• Continuously learning and growth
• International team
• Pinball, PlayStation & much more (on-site)