Key Responsibilities

Computer Vision Pipeline Development:

• Design and implement real-time CV pipelines for object detection, tracking, and classification meeting
• 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/protobufConduct 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: Bachelors 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

• Masters 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)
• Distributed Training: Experience with multi-GPU training (DDP, DeepSpeed) or large-scale data processing (Ray, Dask)