AI/ML Engineer/ Machine Learning Engineer

AI/ML Solution Development:

• Design, build, and deploy production-grade AI/ML models and pipelines addressing business needs such as object detection, container/vehicle recognition, OCR extraction, anomaly detection, predictive analytics, and operational automation.

Computer Vision Engineering:

• Develop CV models using CNNs, EfficientDet, YOLO, RCNN variations, segmentation models, and multi-view camera pipelines tailored for container terminal operations, crane OCR systems, and security applications.

LIDAR & Sensor Data Processing:

• Work with LIDAR point clouds, depth maps, and 3D object detection frameworks to build perception systems for equipment monitoring, spatial analysis, and automated safety workflows.

OCR Engineering:

• Implement OCR and document understanding workflows using Tesseract, EasyOCR, PaddleOCR, Azure Cognitive Services, or custom deep
• learning based OCR architectures.

Model Training & Optimization:

• Perform dataset preparation, augmentation, labeling, hyperparameter tuning, and model optimization for latency, accuracy, and real-time inference.

MLOps & Production Deployment:

• Build scalable CI/CD and MLOps pipelines using Azure Machine Learning, Kubernetes, Docker, MLflow, or similar frameworks for model versioning, monitoring, and retraining.

Data Engineering Collaboration:

• Work closely with data engineering teams to build robust data pipelines, feature stores, and ETL/ELT processes supporting AI/ML use cases.

Integration with Microservices:

• Package AI/ML models as microservices or Minimal API?based inference endpoints for integration into enterprise applications.

Research & Innovation:

• Explore and evaluate emerging AI, CV, LIDAR, and OCR technologies, producing PoCs and technical documentation with pros/cons and recommendations.

Cross-functional Collaboration:

• Work closely with Technical Architects, Software Engineers, QA, and Product Managers to ensure AI/ML solutions fit seamlessly into the architecture and meet business objectives.

Performance Monitoring:

• Implement monitoring, logging, drift detection, and performance evaluation for models running in production.

Documentation & Knowledge Sharing:

• Produce clear technical documentation, architecture diagrams, model cards, and best practices; mentor juniors in AI/ML concepts