Trigyn has a contractual opportunity for a Machine Learning Geospatial Applications . This resource will be working Remotely.

Model Adaptation: Focused on the model architecture, ensuring the model handles the resolution differences between drone (3cm) and satellite (30cm) imagery effectively.
User Interface Enhancement: Upgrade BEAM's user interface to support new functionality and compatibility with the revised model outputs and manage deployment onto municipal infrastructure.
Training and Inference Pipeline: Oversee the development and scaling of the BEAM architecture for fast, robust deployment in new scenarios.
Data Management: Design and oversee the data labelling strategy, working with local experts to ensure accurate classification of different structure types.
Quality Assurance: Implement rigorous validation procedures to ensure accurate structure detection and classification across different urban contexts.
Technical Collaboration: Work closely with Client's technical team to ensure all developments align with ongoing infrastructure improvements and maintain system integrity.
Communication: Present and discuss technical outputs with both subject matter experts and general audiences.
Documentation: Create comprehensive technical documentation, training materials, and user guides for municipal partners.

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).
MLOps knowledge is highly desirable
Experience working with cloud hosting platforms such as Amazon Web Services (AWS), Microsoft Azure, Digital Ocean, Docker, Google Cloud is highly desirable.

UI Development: Experience developing and maintaining user interfaces for technical applications.
Model Development: Demonstrated experience adapting and fine-tuning deep learning models for specific use cases.
Geospatial Analysis: Strong understanding of GIS principles and experience working with aerial/satellite imagery.
Project Implementation: Experience implementing technical solutions in developing country contexts is desirable.
Team Collaboration: Proven ability to work effectively in collaborative technical environments and with non-technical stakeholders.
Documentation: Strong technical writing skills and experience creating user documentation.

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 solid 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

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

Contractual Opportunity For Machine Learning Engineer- GIS (India)

Job Description & Responsibilities