Head of AI Engineering

About the ProjectLead 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 & KPIsBuild 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 months50 % 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 FTE as billable hours allocation at the client's projectsAreas of Responsibility1. Talent & Capability BuildingHire, onboard, and retain A‑player AI Architects and AI engineersEmpower 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 members2. Engineering Excellence & AI‑Native QualityUpdate, 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 & complianceOrchestrate 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 solutionsSkillsAI‑native software engineering & agentic architecturesMLOps automation and observabilityLarge‑scale AWS (SageMaker, Bedrock, EKS) optimisationRegulatory & security compliance for FSIOrganisational design and talent developmentKPI‑driven process improvementStrategic thinking & systems‑level problem‑solvingKnowledgeCore‑banking, insurance, and asset‑management data flows & systemsLLM orchestration patterns and prompt engineering best practicesFoundations of traditional machine learning and ML models training from scratchFinancial‑services regulatory frameworksAWS Marketplace packaging and Advanced‑Tier Partner requirementsCode‑quality measurement (e.g., Codacy) and secure SDLC principlesExperienceLed AI/ML engineering teams in FSI domain while maintaining velocityDelivered production agentic AI systems with 90 % accuracy & < 5 s latencyDeployed autonomous AI agents that automated 40 % of engineering/business processesEstablished, maintained and improved engineering standards and quality measures