AI Engineering Manager

Opis oferty

About deepsense.ai

deepsense.ai is a 120-person AI/ML consultancy with Anthropic and OpenAI partner credentials. For over a decade we’ve delivered applied AI projects for companies like J&J, Sky, John Deere, and GLS — spanning LLM applications, agents, MLOps, and data science. We’re a people-first organization that believes in deep technical craft and real ownership.

About the role

As an AI Engineering Manager you own end-to-end delivery of AI engagements and the people who deliver them. You run delivery governance, keep engagements healthy, and grow your engineers through real feedback and development.

Much of what sets this role apart is client-facing: you’ll run discovery with clients, conduct workshops, and help them prioritise the AI use-cases worth pursuing — then make sure the team delivers them well.

Wymagania

You must have

• 7+ years of professional experience, including 3+ years in a delivery, engineering, or program management role with direct line-management responsibility
• A track record of delivering AI/ML projects for clients, with strong delivery and people leadership
• Strong client-facing and advisory skills — you can run discovery, conduct workshops, prioritise AI use-cases with clients, engage senior stakeholders, and turn ambiguity into a deliverable plan
• A consultancy or professional-services background, with a feel for the delivery–commercial balance
• A hands-on engineering background — you came up building software and can still hold your own in architecture conversations
• Genuine fluency with the modern AI stack (LLMs, RAG, agents, MLOps) — enough to lead engineers credibly
• Excellent English communication skills (C1), written and spoken

You may have

• Product ownership or product management experience (backlog, roadmap, discovery) — a real plus for product-oriented engagements
• Experience building delivery or product practices from scratch, not just running existing ones

Zakres obowiązków

• Own end-to-end delivery of AI/ML engagements — quality, predictability, and business impact
• Be a trusted client-facing partner — translate business goals into deliverable scope and earn senior stakeholders’ confidence
• Run discovery and workshops with clients — understand their business, surface and prioritise the AI use-cases worth pursuing, and turn that into a clear plan the team can deliver
• Run delivery governance — scope, risk, dependencies, staffing — and keep engagement health visible
• Line-manage a team of AI engineers: 1-on-1s, feedback, growth, performance, and team composition — fostering a culture of engineering excellence and innovation
• Support presales: scoping, proposals, and shaping engagements
• Strengthen our delivery practice — playbooks, standards, and knowledge sharing across teams