Head of AI Engineering

Neurons Lab; Roma; In loco; EUR 80.000 - 110.000; Tempo pieno

Descrizione del lavoro

A leading AI solutions provider in Italy is seeking a Head of AI Engineering to lead the development of compliant and reusable AI systems for financial services. This role involves managing a talented team of architects and engineers, driving automation and quality in engineering processes. The ideal candidate has extensive experience in AI engineering leadership with proven results in the financial sector. Candidates should be strategic thinkers with a strong understanding of regulatory compliance and talent development.

Competenze

• Experience leading AI/ML engineering teams of 15+ in the FSI domain.
• Proven track record delivering production AI systems with ≥ 90% accuracy.
• Familiarity with AWS services like SageMaker and EKS.

Mansioni

• Build a high-quality AI engineering function for FSI clients.
• Hire and mentor AI architects and engineers.
• Automate workflows and monitor engineering health indicators.

Conoscenze

AI-native software engineering & agentic architectures

MLOps automation and observability

Large-scale AWS optimization

Regulatory & security compliance for FSI

Organizational design and talent development

KPI-driven process improvement

Strategic thinking & systems-level problem-solving

Descrizione del lavoro

Join to apply for the Head of AI Engineering role at Neurons Lab

Lead our AI engineering team to transfer systems for financial services. You’ll manage a team of AI architects and engineers while automating workflows with AI agents. We’re creating production‑ready AI solutions for banks, insurance companies, and investment firms—from customer support bots to document automation and productivity assistants.

Objective & KPIs

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

• Develop PoCs in 2 weeks; develop production solutions in 2 months
• ≥ 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 personalized 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; optimize utilization 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 (SageMaker, Bedrock, EKS) optimisation
• Regulatory & security compliance for FSI
• Organizational 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

• Director

Employment type

• Full‑time

Job function

• Engineering and Information Technology

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