Strong problem-solving skills, attention to detail, and eagerness to learn new tools and technologies.
Ability to work independently with minimal supervision.
Requirement: Candidates must submit a portfolio of past web projects (university assignments, personal projects, or freelance work) upon application.
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Experience with CCTV or video surveillance systems — IP cameras, NVRs, video management systems, or video analytics platforms.
Experience with on-premise server and software deployment — Linux, containers (Docker), networking.
Demonstrated experience using AI, machine learning, and AI agent tools to improve delivery efficiency, reporting, or risk management — and interest in how AI agents are changing the delivery role itself.
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You have the mindset of a Staff Engineer + AI Engineer + Distributed Systems Engineer. You enjoy problems where the architecture is not obvious and conventional software engineering is not enough. You move comfortably between frontend → backend → infrastructure → data → LLMs → agents → evaluation → optimization. You do not just ask "how do we implement this?" — you ask "how do we design this so the system can eventually figure out the best way to implement and operate it itself?"
Anthropic's Claude ecosystem · Production AI agents · MCP (Model Context Protocol) · LangGraph or equivalent orchestration frameworks · Temporal or workflow orchestration · Reinforcement learning concepts · Automated experimentation · AI evaluation frameworks · Vector databases · Autonomous coding agents · Browser/computer-use agents · Multi-agent systems · AI-native SaaS products · Open-source contributions · Leading technical architecture
This is a 10+ year engineering role. You own architecture rather than implement tickets. You make independent technical decisions, identify architectural weaknesses proactively, mentor engineers, establish engineering standards, debug complex production systems, think about scalability from day one, understand the trade-offs between performance, cost and reliability, translate ambiguous business problems into technical systems, prototype rapidly and productionize what works, and challenge assumptions when necessary.