Must:

Hands-on experience with Machine Learning and Deep Learning model development not just application-level integration

Familiarity with computer vision architectures relevant to building/object detection (e.g., U-Net, HRNet-style models)

Experience working with geospatial/aerial imagery data

Proven track record of building and fine-tuning models from scratch (not only deploying pre-built/third-party models)

A hybrid profile: ML/DL engineer with robust software engineering skills (rather than a Software Engineer who happens to know ML

Prior experience owning end-to-end ML lifecycle: research/experimentation training/fine-tuning packaging deployment scaling

• Qualifications: Master's degree or higher in Computer Science, Data Science, Remote Sensing, or a related field, with focus on machine learning applications (or demonstrable equivalent work experience).




• A minimum of 5 years of experience in Data science with at least one project working with Geospatial technologies.
• Deep Learning: At least 3 years of experience applying deep learning to computer vision problems, particularly in remote sensing or geospatial applications.
• Technical Stack: Proficiency in PyTorch or a related DL framework, and experience with geospatial libraries (GDAL, rasterio, geopandas).

Nice to have:

Experience with model training, tuning, and optimization for production use cases

Ability to package and productionize ML models for scaled deployment across use cases/geographies

Experience building CI/CD workflows and automation pipelines

Ability to optimize models for both local/on-premise hosting and cloud-based deployment

📌 Machine Learning Engineer, Geospatial Applications (India)
🏢 Trigyn Technologies
📍 India