Job Description
Our Client is currently hiring a Technical Lead to join their amazing team
About the role:
The Hub Technical Lead runs the day-to-day execution of the AI Innovation Hub in Lisbon and owns the coherence of the technical product across every layer — AI infrastructure, applied AI/ML, product engineering and AI advisory. The role sits between the Hub Executive Sponsor and the founding specialists, and the person in it ships alongside them.
This is not a purely managerial position. The Hub starts small, the build is greenfield (0→1) and the pace is startup-fast. We are looking for a leader who has both built and led — someone who earns credibility through contribution, not through a title.
The AI Innovation Hub is a new initiative sponsored by a large international group, deliberately structured outside the group’s traditional operating model so it can move at startup speed, own its technical decisions, and ship production-ready AI from day one. Mission: design, build and deploy AI-driven solutions that can be scaled globally.
The team brings together different skills and disciplines across cloud infrastructure (CI/CD, observability, cost), applied AI/ML (RAG pipelines, LLM integration, evaluation frameworks), full-stack engineering (operator dashboards, chat UIs, playbook editors), agentic AI advisory, data engineering and applied research. Headcount will grow aligned with Hub needs, and the Lead is expected to shape both the next hires and the structure around them.
Key responsibilities:
Technical leadership
- Own the end-to-end technical roadmap across AI infrastructure, applied AI/ML and product layers
- Ensure architectural coherence: the Platform Engineer’s cloud stack, the AI/ML Engineer’s pipelines and the Full-Stack Engineer’s interfaces must work as a unified production system.
- Facilitate — and where delegated, make — critical technical trade-offs (quality, latency, cost) in alignment with the Hub Executive Sponsor.
- Conduct technical reviews of designs, pull requests and deployment plans.
Delivery and project ownership
- Own delivery milestones from initial PoC (Proof of Concept) through to production-scale rollout.
- Manage cross-team dependencies and sequencing — infrastructure must be ready before AI pipelines can scale; interfaces must reflect agent behaviour in real time.
- Translate the Sponsor’s strategy into sprint goals without adding process overhead.
- Surface risks and blockers proactively; propose solutions before they become crises.
People leadership
- Directly lead a multi-disciplinary team of specialists spanning cloud infrastructure, AI/ML, full-stack engineering, agentic AI advisory, data engineering and applied research.
- Own hiring and onboarding of additional engineers as the Hub scales.
- Foster a culture of ownership, accountability, technical rigour and psychological safety.
- Create the conditions for each specialist to do their best work: clear priorities, minimal context-switching, fast decision cycles.
Stakeholder management
- Serve as the primary interface between the technical team and the Hub Executive Sponsor on all operational matters.
- Communicate progress, risks and technical realities clearly to non-technical stakeholders.
- Gradually build the bridge into the sponsoring group’s broader organisation as the Hub scales.
What they're looking for:
Hard Skills:
- Applied AI/LLM Systems - Sufficient depth to review RAG architecture, evaluate prompt strategies, interpret evaluation metrics (hallucination rate, retrieval precision) and challenge the AI/ML Engineer’s technical choices. Direct hands-on experience shipping at least one LLM-powered production system.
- Cloud Infrastructure - Cloud architecture fluency across at least one major hyperscaler (AWS, Azure or GCP) with a cloud-agnostic mindset — the Hub stack is designed to run on multiple platforms. Solid grasp of networking, container orchestration, managed databases, identity, CI/CD and observability. Able to review IaC plans (Terraform, Pulumi or equivalent) and catch security or cost issues.
- Software Engineering - Strong engineering foundations: Python and/or TypeScript, REST API
- design, async patterns, testing methodology. Credible in code reviews across both backend AI services and frontend React / Next.js.
- Delivery Frameworks - Experience running lean agile delivery in small, high-velocity teams. Comfortable with iterative planning, OKRs and metric-driven decision-making — without imposing heavy process on a startup-scale team.
- System Design - Can design and critique end-to-end production AI systems: data pipelines, model serving, evaluation loops, client-facing interfaces. Understands the full stack at architecture level.
Soft Skills:
- Player-manager balance equally comfortable in a design review or a 1:1. Does not retreat into pure management. Knows when to delegate and when to roll up sleeves.
- Comfort with ambiguity: energised — not paralysed — by building from zero. No legacy systems, no established playbooks, no template to copy.
- Structured communication translates technical complexity into clear business language for the Sponsor, and business context into concrete engineering priorities for the team.
- Opinionated but open: holds strong technical views loosely. Challenges specialists constructively. Can disagree and commit once a decision is made.
- Accountability culture: builds a team where ownership is real, deadlines are respected and problems surface early rather than late.
- Coaching instinct invests in the growth of each team member. The early hires set the bar for everyone who follows.
Nice to Have
- Prior experience in Business Process Outsourcing, enterprise services or Customer Experience automation
- Published work, conference talks or open-source contributions in AI engineering.
- Background in a consultancy or advisory context: able to structure problems and communicate findings rigorously.
- Familiarity with compliance frameworks relevant to enterprise AI: SOC 2, GDPR data residency, audit trails.
- Network in the Lisbon or European AI/tech community
- Experience scaling a Hub or product team beyond its founding phase.
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