Head of Engineering

About the ProjectLead, 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 & 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: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 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 solutions SkillsAI‑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‑solving KnowledgeCore‑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 principles ExperienceLed AI/ML engineering teams 15 → 50 + 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 measuresOriginally posted on Himalayas

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