Head of Engineering

🚀 About the Project Lead, scale, and continuously reinvent an AI‑native engineering organisation by empowering a high‑leverage team of AI architects and engineers and automating repeatable engineering workflows with autonomous AI agents , that turns breakthrough ideas into resilient, production‑grade agentic AI systems across both client work and the company’s own product portfolio for global financial‑services institutions (banking, insurance, investment management) - spanning use‑cases customer support agents, internal productivity assistants, documents workflow automation and others - compounding revenue, IP leverage, and long‑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.

KPIs:

• 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 (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