AI systems engineer

Job Summary

We are looking for an AI Systems Engineer – Computer Vision & MLOps with 2–3 years of hands-on experience building and deploying production-grade AI systems. The role involves developing and optimizing computer vision models, building robust backend services and data pipelines, and managing end-to-end ML workflows from data preparation and model training to deployment, monitoring, and scaling.

The ideal candidate should be comfortable working with GPU infrastructure, edge devices, real-time camera systems, and production environments. Experience with object detection, multi-object tracking, MLOps, and pipeline automation is essential. Familiarity with NIR imaging, spectral data fusion, and industrial material classification is a plus.

Experience

2–3 years in Machine Learning Engineering, Computer Vision, MLOps, or related fields.

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related engineering field.

• Strong programming and software engineering fundamentals, with practical experience building and deploying AI solutions.

• Equivalent practical experience demonstrating the required technical skills is also valued.

Must Have

• Strong Python fundamentals and experience writing clean, modular, production-quality code.

• Hands-on experience with object detection and multi-object tracking models, such as YOLO, RT-DETR, or similar architectures.

• Experience handling messy real-world data, including occlusion, class imbalance, varying lighting conditions, and domain shift.

• Experience building end-to-end ML pipelines, covering data collection, annotation workflows, preprocessing, model training, evaluation, inference, and deployment.

• Strong backend development experience with frameworks such as Flask or FastAPI, including API development and integration of ML models into production applications.

• Hands-on experience with MLOps, including experiment tracking, model versioning, automated deployment workflows, monitoring, and model lifecycle management.

• Experience deploying and optimizing models on GPU hardware, with an understanding of inference latency, throughput, and resource utilization.

• Experience building reliable, scalable data processing and inference pipelines for real-time or batch workloads.

• Experience deploying AI solutions on edge devices, including camera integration, embedded computing, and real-time inference.

• Familiarity with Docker, Linux, CI/CD, cloud infrastructure, and production system monitoring.

• Experience scaling computer vision systems across multiple cameras, edge devices, or industrial environments.

Good to Have

• Knowledge of sensor fusion techniques for combining visual and spectral information for material classification.

• Experience with Near-Infrared (NIR), hyperspectral or multispectral imaging, NIR camera integration, or spectral data fusion with RGB vision.

• Experience working with industrial automation, waste segregation, recycling, or material classification systems.

Who You Are

• You think beyond model accuracy and care about reliability, latency, maintainability, and production performance.

• You can take ownership of the entire ML lifecycle — from data and model development to backend integration, deployment, and monitoring.

• You are comfortable debugging issues across models, data pipelines, APIs, infrastructure, and hardware.

• You thrive in a fast-moving, early-stage environment where you can build systems from the ground up and make meaningful technical decisions.