Head of AI Engineering Lisboa

Head of AI Engineering
Lisboa
Lisboa, Lisboa, Portugal

Overview

Head of Engineering role at Neurons Lab. Lead, scale, and continuously reinvent an AI-native engineering organisation by empowering a
- leverage team of AI architects and engineers and automating repeatable engineering workflows with autonomous AI agents, that turns breakthrough ideas into resilient,
- grade agentic AI systems across both client work and the company’s own product portfolio for global
- services institutions (banking, insurance, investment management) - spanning
- cases customer support agents, internal productivity assistants, documents workflow automation and others - compounding revenue, IP leverage, and
- term strategic advantage.

Objective & KPIs

Build a self‑sustaining AI‑native engineering function that delivers high‑quality, compliant, and reusable agentic solutions for FSI clients while maximising automation and team leverage.

• Mean lead‑time from prototype commit to production ≤ 5 days.


• ≥ 50 % internal engineering workflows fully automated by autonomous AI agents (baseline FY‑2025 audit).


• ≥ 75 % code/component reuse across new projects.


• Production model accuracy ≥ 90 %, latency < 5 s, Codacy grade upgraded from B → A.


• Maintain 0. 375-0. 5 FTE as billable hours allocation at the client’s projects

Areas of Responsibility

• Talent & Capability Building


• Hire, onboard, and retain A‑player AI Architects and AI engineers


• Empower AI architects and engineers with clear decision rights, context, and AI‑native tooling so they can execute autonomously and at speed.


• Implement a skills‑matrix and personalised growth plans; coach next‑generation tech leads.


• Make decisions on promotion based on performance reviews anchored in objective contribution metrics.


• Promote a culture of continuous learning (regular "Agentic AI dojo", conference sponsorships, internal certifications).


• Provide technical oversight through senior AI Architects across all client engagements; sign off on architecture and go‑live readiness while mentoring them to own delivery.


• Staff projects with the right talent mix; optimise utilisation of core team members


• Engineering Excellence & AI‑Native Quality


• Update, automate, and collect AI engineering health indicators - including solution accuracy, latency, model drift, cost efficiency, and code quality - via a fully instrumented MLOps telemetry stack (CI/CD, feature store, observability, drift alerts).


• Establish and iterate the AI‑native SDLC: LLM‑assisted coding & test generation, agentic design patterns, self‑healing pipelines, prompt‑ops, red‑teaming, security & compliance


• Orchestrate autonomous AI agents to automate internal engineering and business routines such as environment provisioning, compliance evidence capture, cost optimisation, and status reporting.


• Maintain reference architectures and reusable component libraries; achieve ≥75% code reuse across all new work.


• Convert learnings from services projects into IP that reduces future build effort by> 40 %.


• Own the design, packaging, and optimisation of Neurons Lab solutions

Skills

• AI‑native software engineering & agentic architectures


• MLOps automation and observability


• Large‑scale AWS (Sage
  Maker, Bedrock, EKS) optimisation


• Regulatory & security compliance for FSI


• Organisational design and talent development


• KPI‑driven process improvement


• Strategic thinking & systems‑level problem‑solving

Knowledge

• Core‑banking, insurance, and asset‑management data flows & systems


• LLM orchestration patterns and prompt engineering best practices


• Foundations of traditional machine learning and ML models training from scratch


• Financial‑services regulatory frameworks


• AWS Marketplace packaging and Advanced‑Tier Partner requirements


• Code‑quality measurement (e. g. , Codacy) and secure SDLC principles

Experience

• Led AI/ML engineering teams 15 → 50+ in FSI domain while maintaining velocity


• Delivered production agentic AI systems with ≥ 90 % accuracy & < 5 s latency


• Deployed autonomous AI agents that automated ≥ 40 % of engineering/business processes


• Established, maintained and improved engineering standards and quality measures

Seniority level

• Executive

Employment type

• Full-time

Job function

• Engineering and Information Technology

Industries

• IT Services and IT Consulting