Principal AI Engineer

Al Khobar, KSA | Full-time | Principal individual contributor | Hands-on technical leadership

Millions of people work on large industrial and construction sites where a missed hazard can cost a life. Our Client's safety platform is mandated on some client sites — a detection we get wrong has physical and contractual consequences, not just a bad metric. You will shape the AI systems that turn live site video into timely, trustworthy safety insight, built to hold up under real field conditions rather than benchmark conditions.

This is a principal-level individual-contributor role for an engineer who combines deep computer-vision and applied-AI expertise with broad technical influence. You will set architecture and quality standards, solve the hardest technical problems, and stay directly involved in implementation, experimentation, and production operation.

You will guide senior engineers through design reviews, pairing, and technical mentorship, but you will not be responsible for hiring, performance management, or running squad ceremonies. Your authority comes from technical judgment, high-leverage contributions, and the ability to align teams around a sound engineering direction.

This is a deliberately narrow and deep role:
video is the product
, not a feature of it. Our perception stack runs vision-language and computer-vision analysis over live CCTV, fuses spatial reasoning, and maps what it sees to OSHA-aligned hazard categories.

• You can explain the perception architecture end to end, including its highest-risk technical assumptions, its quality gaps, and the edge-versus-cloud trade-offs it currently makes.
• A measurable evaluation baseline and an automated regression gate are in place for detectors, trackers, multimodal models, prompts, and agent behaviour. Today our evaluation rigour lives in an offline, manually-run process — no accuracy regression test blocks a merge. Building that gate is one of the clearest mandates of this role, and you should expect to build it rather than inherit it.
• You have shipped at least one material improvement to model quality, latency, reliability, or operating cost, and validated it under representative site conditions — with the before-and-after numbers to show it.
• The team is using clear architecture decisions, reusable technical patterns, and production feedback to make faster, safer changes.
• Product, Field Engineering, and leadership trust you as the technical authority on the AI system’s behaviour, its limitations, and its roadmap.

• Technical direction for the perception stack — including where vision-language and multimodal models add value, and where a lighter purpose-built detector is the right call on cost, latency, and annotation burden.
• The end-to-end model lifecycle — problem framing, data strategy, experimentation, fine-tuning, evaluation, deployment, monitoring, and continuous improvement.
• System-level quality across edge and cloud — balancing accuracy, latency, cost, throughput, privacy, and intermittent site connectivity.
• Responsible-AI and safety controls — confidence policies, human review for high-severity events, explainability, audit trails, and fail-safe behaviour. High-severity events need a fast path; that path trades latency against safety, and you will own where that line sits.
• Technical coherence across computer vision, agentic AI, video infrastructure, device integration, APIs, and production observability.

• Design and build production perception systems for object detection, segmentation, tracking, event understanding, and open-ended visual reasoning — including spatial and depth reasoning, not just 2D boxes.
• Develop agentic AI systems…