الخبرة : 0-3 سنة
الراتب : غير مذكور
المكان : السعودية العربيه
Key Responsibilities
Computer Vision Pipeline Development
Design and implement real-time CV pipelines for object detection, tracking, and classification meeting <100ms p99 latency SLOs
Build multi-object tracking systems across camera feeds with re-identification and trajectory forecasting
Develop preprocessing pipelines for video streams (frame extraction, normalization, augmentation) with error handling and backpressure mechanisms
Implement annotation workflows and active learning loops to continuously improve model quality
Model Engineering & Optimization
Fine-tune and evaluate SOTA open-source models (YOLO, EfficientDet, DETR families) on domain-specific datasets
Optimize inference throughput: batching strategies, model quantization (INT8/FP16), ONNX/TensorRT conversion, and multi-GPU orchestration
Build A/B testing frameworks to measure model performance (mAP, FPS, recall@IOU) in production
Maintain model registry with versioning, lineage tracking, and rollback capabilities
Production ML Infrastructure
Architect scalable ML services exposing REST/gRPC APIs with authentication, rate limiting, and circuit breakers
Containerize models and services (Docker) with CI/CD pipelines for automated testing and deployment
Implement monitoring dashboards tracking inference latency, GPU utilization, prediction confidence distributions, and data drift
Own incident response: debug production issues, conduct root-cause analysis, implement permanent fixes
Software Engineering Excellence
Write maintainable Python code with type hints, unit/integration tests (pytest), and API documentation
Design clear data contracts between services; validate schemas with Pydantic/protobuf
Conduct thorough code reviews focusing on performance, maintainability, and ML best practices
Document system architecture, model cards, and operational runbooks
Collaboration & Mentorship
Partner with data engineers on annotation tooling, dataset pipelines, and feature stores
Work with DevOps to optimize Kubernetes deployments, autoscaling policies, and cost efficiency
Mentor junior engineers on CV fundamentals, debugging techniques, and production ML patterns
Present technical deep-dives to cross-functional stakeholders
Minimum Qualifications
Education: Bachelor's in Computer Science, Computer Engineering, Electrical Engineering, or related field
Experience: 3-6 years building and deploying ML systems in production environments
Computer Vision: Proven track record shipping CV solutions (object detection, segmentation, tracking, or pose estimation) handling real-world data
Python Proficiency: Strong software engineering skills—clean code, testing (pytest/unittest), packaging, virtual environments, type hints
Model Deployment: Experience serving models via REST/gRPC APIs with frameworks like FastAPI, Flask, or TorchServe
Infrastructure: Hands-on with Docker, CI/CD tools (GitHub Actions, GitLab CI), and cloud platforms (AWS/Azure/GCP) or on-prem GPU clusters
Performance Tuning: Practical experience profiling code (cProfile, py-spy), optimizing memory usage, and reducing inference latency
Preferred Qualifications
Master's degree in Computer Science, Data Science, Machine Learning, or related field
Advanced CV: Multi-object tracking (SORT, DeepSORT, ByteTrack), trajectory forecasting, or video understanding models
Model Serving: Experience with Triton Inference Server, TorchServe, vLLM, or TensorRT optimizations
LLM/RAG Systems: Built retrieval-augmented generation pipelines using vector databases (Pinecone, Weaviate, Milvus) and embedding models
Edge Deployment: Optimized models for edge devices (NVIDIA Jetson, Coral TPU) with latency/power constraints
MLOps Maturity: Worked with experiment tracking (MLflow, Weights & Biases), feature stores (Feast, Tecton), or Kubernetes operators (KubeFlow, Seldon)