Tech Lead

Position details

Seniority: Director

Work format: Remote, Hybrid

Location: Any, Cyprus, Serbia

Define the technology strategy and target architecture of the project.
Design an end-to-end AI-powered game production pipeline and identify the key human-in-the-loop checkpoints.
Select AI models, agentic frameworks, development tools, and the infrastructure stack.
Design AI-agent interactions and establish effective management of context, memory, tools, and generated artifacts.
Implement evaluation frameworks, guardrails, and observability for AI-generated outputs.
Build AI-powered workflows for coding, testing, code review, refactoring, and documentation.
Establish AI-driven processes for creating UI, 2D/3D assets, animations, and visual effects.
Design a shared game engine, SDK, backend services, and reusable technology components.
Build cloud and production infrastructure, CI/CD pipelines, Infrastructure as Code, monitoring, and incident management processes.
Ensure scalability, high availability, security, latency control, and efficient management of AI infrastructure costs.
Build and grow the engineering organization while remaining personally involved in development and technical experimentation.

Experience as a CTO, VP of Engineering, Head of Engineering, Principal Engineer, or Software Architect.
Proven track record of building a technology product or platform from the ground up.
Strong expertise in system architecture combined with hands-on production engineering experience.
Experience building and scaling engineering teams, with a willingness to remain hands-on.
Practical experience implementing AI in product development, software engineering, or digital content production.
Strong knowledge of LLMs, multimodal models, AI agents, RAG, tool calling, and agent orchestration.
Expertise in prompt, context, and memory management, as well as evaluation, guardrails, observability, and human-in-the-loop workflows.
Experience integrating AI with repositories, CI/CD pipelines, task trackers, documentation platforms, and internal systems.
Strong knowledge of cloud infrastructure, Kubernetes, Infrastructure as Code, CI/CD, and monitoring.
Understanding of GPU infrastructure and self-hosted models, including the trade-offs between quality, performance, cost, and reliability of AI solutions.
Strong awareness of the current landscape of text, reasoning, coding, vision, video, animation, audio, and open-source AI models.