Neurons Lab
Neurons Lab is a globally distributed AI R&D company that helps deep tech innovators to accelerate data-driven products development and launch. Our team has expertise in fundamental sciences, full-stack AI/ML engineering, and product design. Such a rare combination and access to scarce talent allows Neurons Lab to build disruptive solutions for clients in HealthTech and EnergyTech industries. Neurons Lab operates within a proprietary delivery framework that is tailored to the innovation environment: fierce competition, tight timelines, little-to-none datasets, and the necessity to generate novel solutions.
51 - 200 employees
💰 Corporate Round on 2022-10
Head of Engineering
August 11
Neurons Lab
Neurons Lab is a globally distributed AI R&D company that helps deep tech innovators to accelerate data-driven products development and launch. Our team has expertise in fundamental sciences, full-stack AI/ML engineering, and product design. Such a rare combination and access to scarce talent allows Neurons Lab to build disruptive solutions for clients in HealthTech and EnergyTech industries. Neurons Lab operates within a proprietary delivery framework that is tailored to the innovation environment: fierce competition, tight timelines, little-to-none datasets, and the necessity to generate novel solutions.
51 - 200 employees
💰 Corporate Round on 2022-10
📋 Description
• 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 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: 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; Promote a culture of continuous learning; Provide technical oversight through senior AI Architects across all client engagements; staff projects with the right talent mix; optimise utilisation of core team members • 2. Engineering Excellence & AI‑Native Quality - Update, automate, and collect AI engineering health indicators; Establish and iterate the AI‑native SDLC; Orchestrate autonomous AI agents to automate internal engineering and business routines; Maintain reference architectures and reusable component libraries; achieve ≥75% code reuse; Convert learnings from services projects into IP; Own the design, packaging, and optimisation of Neurons Lab solutions • 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
🎯 Requirements
• 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 • 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 • 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
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