Head of AI Engineering

Head of AI Engineering

🚀 About the Project

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.

You'll turn breakthrough ideas into reliable systems that work at scale, delivering for both client projects and our own products. The goal is compound growth: building revenue, developing IP, and creating long-term competitive advantage in the financial services space.

This role combines people leadership with technical strategy—empowering your team while architecting the future of AI-driven financial technology.

🎯 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.

KPIs:

• 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

1. 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

1. 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

null

🛠 Skills

• AI‑native software engineering & agentic architectures
• MLOps automation and observability
• Large‑scale AWS (SageMaker, 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