Role: AI/ML Engineer | Machine Learning Engineer
Industry Type: IT Services & Consulting
Department: Engineering - Software & QA
Employment Type: Full Time, Permanent
Role Category: Software Development
UG: Any Graduate
PG: Any Postgraduate
- 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