The Role
This is a build-the-future role, not a maintenance role. As Technical Director, you will own technical direction across a multi-platform AI application system, including VR/PC/Web simulation (Unreal Engine), a web platform for content authoring, e-learning and administration, and learning analytics systems.
You are part of a leadership triad alongside the Director of Product Marketing and the Director of Product Management, sitting at the intersection of product ambition, technical reality, and system scalability. This is a player-coach role with real influence across product, engineering, and architecture.
What You'll Do
- Own Technical Direction:
- Simplify, modularize, and future-proof a multi-platform system growing in complexity, spanning VR/PC/Web simulation, web authoring tools, and analytics infrastructure.
- Define technical standards and architecture that raise the engineering ceiling for the entire team.
- Shape AI Strategy in Practical Ways:
- Deliver AI-generated scenarios, adaptive learning systems, and AI-assisted development workflows.
- Focus on turning AI into shipped product value.
- Bring Discipline to Engineering Execution:
- Improve estimation accuracy, delivery predictability, and development velocity.
- Establish scalable engineering patterns and a culture of quality and accountability.
- Build for Scale Before It Breaks:
- Lead improvements in system reliability, uptime, observability, and performance as the company moves toward its next level of customer growth.
- Turn Data Into a Core Product Capability:
- Drive analytics architecture to enable measurement of learner performance, simulation effectiveness, and product usage.
- Help establish Lumeto as a data-driven platform.
- Support Product Expansion:
- Ensure tight integration between simulation, competencies, and learning workflows.
- Enable rapid iteration using modern AI development methodologies.
What We're Looking For
Core Requirements (Must-Have)
- Proven track record building and operating scalable, cloud-native distributed systems in production at significant scale - real traffic, real orchestration, real failure modes.
- Deep backend expertise across APIs, services, and the data layer, with the ability to be genuinely hands-on and write production-quality code, not only review it.
- Strong infrastructure and orchestration maturity: Kubernetes at scale, CI/CD, infrastructure-as-code, observability, and cost-aware scaling.
- Platform security as a default - authentication, secrets management, and network/data security designed from the start.
- Modern web platform architecture, including real-time and streaming systems where relevant.
- Deep Microsoft Azure experience, including sound judgment on managed-service-versus-build tradeoffs at scale.
- Agentic / AI Engineering
- Recent, hands-on experience building agentic and LLM-powered systems in production, including tool-calling, orchestration, evaluation, and the infrastructure to serve them reliably.
- Strong understanding of the latency, cost, and reliability tradeoffs of model-backed systems at scale.
- Leadership & Communication
- Sets technical direction, standards, and architecture; raises the engineering bar and mentors others to multiply impact.
- Excellent communicator who can translate complex technical tradeoffs for non-technical stakeholders and align a team.
- Genuine fluency working with data - instrumentation, metrics, and data-driven decision-making.
Nice-to-Have
- Proficiency in C++.
- Background in large-scale real-time systems or interactive/game infrastructure, where web and cloud platform skills supported high-throughput, low-latency workloads.
- Real-time streaming and low-latency systems experience.
- Exposure to healthcare, simulation, or education technology.
Challenges You'll Face
- Expect to navigate accumulated technical debt while simultaneously driving forward.
- Balancing hands-on technical contribution with the leadership responsibilities of setting direction and growing others.
- Operating in a fast-moving, high-growth environment where priorities can shift and delivery expectations are high.
What Success Looks Like
- In the first 90 days: You have a clear picture of the architecture, have identified the highest-leverage technical improvements, and have earned the trust of the engineering team.
- At 6 months: Engineering delivery is more predictable, key reliability and scalability improvements are underway, and your fingerprints are on the AI strategy.
- At 1 year: The platform is measurably more scalable and observable, the team is levelled up, and Lumeto's AI capabilities are shipping real product value.
You will shape the platform before it scales beyond control. You will influence both the current system and the next generation of products. You will work at the intersection of AI, simulation, and human performance - a combination that is rare, technically demanding, and carries genuine societal impact.
