Head of AI Engineering
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.
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.
- Develop PoCs in 2 weeks, 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
- 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 engineers; empower them with decision rights and AI‑native tooling; implement skills‑matrix, growth plans; coach next‑generation tech leads; promote a culture of continuous learning.Technical Oversight : provide senior AI architectural sign‑off across all client engagements; mentor delivery staff.Engineering Excellence & AI‑Native Quality : automate AI engineering health indicators via MLOps telemetry; establish AI‑native SDLC; orchestrate autonomous AI agents for internal routines; maintain reference architectures and reusable libraries; convert service learnings into IP to reduce build effort by >40 %.
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 model 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 &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
Industries
IT Services and IT Consulting
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