Engineering Manager, Computer Vision and AI - SiteGuard Squad Lead

About WakeCap

WakeCap develops a connected-worker platform tailored for extensive construction and industrial project sites. Their solutions include smart helmets, anchors, and gateways that integrate with a real-time SaaS platform, supplying site teams with location data, safety compliance oversight, automated mustering, and productivity insights. The platform is deployed across prominent projects in the GCC region.

Role Overview

The SiteGuard product is WakeCap's advanced computer vision safety solution which detects the lack of PPE adherence, hazards, and unsafe behaviors using site cameras in real time. This system operates both on edge devices near the cameras and on the cloud, providing uniform detection results regardless of deployment.

As the SiteGuard Squad Lead, you will be responsible for the entire technology stack—from detection models and camera interfacing agents through evaluation and MLOps pipelines to the end-user product interfaces that translate raw detections into actionable safety measures for HSE managers and site supervisors. You will lead a compact, expert, cross-disciplinary team with autonomy and direct influence on the product's impact.

Key Responsibilities

• Oversee the complete lifecycle of detection functions covering PPE compliance, hazard identification, and unsafe behavior recognition—from problem definition through training, evaluation, to deployment in production.
• Develop and implement model strategies spanning classical detectors (YOLO series), transformer-based detectors (RT-DETR, Co-DETR, GroundingDINO), zero/few-shot techniques (CLIP, DINOv2, SAM 2), and visual-language models (Gemini Vision, GPT-4V, Qwen-VL) when they offer superior performance.
• Manage edge and cloud camera agents ensuring optimized inference on limited hardware via quantization, INT8/FP16, TensorRT, ONNX, and maintain consistent outputs across deployment scenarios.
• Lead evaluation and MLOps activities including maintaining a golden labeled dataset as the authoritative reference, enforcing quality gates to prevent regressions, experiment tracking, model registries, dataset version control, and monitoring metrics such as drift, cost, and accuracy.
• Drive recruitment, performance management, culture development, and squad delivery.

Desired Qualifications and Skills

• Over 10 years of engineering experience with a clear career progression from senior individual contributor to leadership roles; at least 5 years managing engineering teams including hiring, performance reviews, and managing complex conversations.
• Proven track record of computer vision and machine learning systems deployed in production, particularly involving high-throughput, asynchronous processing of video and media.
• Advanced Python programming expertise with an async-first approach using frameworks such as asyncio and FastAPI, emphasizing reliability and observability.
• Strong computer vision fundamentals, including expertise in classical and transformer-based detection and segmentation, video frame processing tools like OpenCV and FFmpeg, and foundation models such as CLIP, DINOv2, and SAM 2.
• Experience with multimodal and visual-language model APIs (Gemini/Vertex, OpenAI, Anthropic) including prompt engineering, JSON-schema constrained outputs, caching, and model-specific tuning.
• Expertise in edge inference optimizations using ONNX, TensorRT, quantization techniques on platforms such as Jetson or Hailo.
• Hands-on know-how of MLOps practices including experiment tracking, model registries, dataset versioning, and continuous integration for model evaluation.
• Daily practical use of AI coding assistants like Claude Code and Codex, with a solid understanding of the current AI model landscape.
• Excellent command of written and spoken English is mandatory.

Preferred Additional Experience

• Background in safety, surveillance, or video analytics within industrial or construction environments; familiarity with OSHA/EHS standards.
• Knowledge of synthetic data techniques such as ControlNet or Stable Diffusion for training data augmentation.
• Experience with LLM operations and observability for services supported by models.
• Proficiency with agentic frameworks like LangChain, LlamaIndex, AutoGen applied in safety workflows.
• Handling on-premise or edge computing solutions under variable connectivity conditions.

Compensation and Benefits

• Competitive salary combined with performance bonuses and equity participation.
• A high-autonomy leadership position helping to build an AI-driven safety product from inception, with significant technical ownership.
• Support for relocation for candidates moving to Saudi Arabia.
• Health insurance coverage, annual flights, and other standard employee benefits provided by WakeCap.