Sr. Dev: AI

This is a unique opportunity to join our team as a Senior Machine Learning Developer. We are seeking an expert in AI/ML with experience in designing, developing, and deploying large-scale enterprise-grade solutions.

The role involves contributing to the development of AI-powered GIS platforms, multimodal and geospatially aware models, and real-time feature detection systems.

Key Responsibilities:

• Designing and implementing AI/ML solutions in large-scale production environments
• Developing strong programming skills in Python with practical use of TensorFlow, PyTorch, Keras, scikit-learn, NumPy, Pandas, and OpenCV
• Implementing Computer Vision architectures, including YOLO, SAM, U-Net, and other CNN-based models for real-time object detection, segmentation, and feature extraction
• Expertise in image classification using advanced CV models (e.g., ResNet, VGG, YOLOv5/v8, EfficientNet)
• Deep understanding and implementation experience of machine learning algorithms (supervised, unsupervised, and reinforcement learning) for classification, regression, and clustering
• Experience in Generative AI and Large Language Models (LLMs), including transformers, diffusion models, multimodal pipelines, and speech-to-text/NLP solutions
• Strong foundation in mathematics, statistics, data structures, algorithms, and optimization techniques
• Hands-on experience with RNNs/LSTMs, hybrid neural architectures, and temporal/spatiotemporal modeling
• Practical knowledge of Agentic AI systems (AI agents, multi-agent workflows, or autonomous agent design)
• GIS domain expertise – demonstrated ability to integrate AI/ML with geospatial datasets, satellite imagery, and spatial analytics to deliver practical solutions
• End-to-end experience in AI/ML solution lifecycle – from data preprocessing, model training, evaluation, deployment, and scaling
• Hands-on exposure to geospatial toolkits/libraries (e.g., GDAL, GeoPandas, QGIS, ArcGIS APIs) and their integration with ML workflows

Requirements:

• 3–5 years of hands-on experience in implementing AI/ML solutions in large-scale production environments
• Strong programming proficiency in Python with practical use of TensorFlow, PyTorch, Keras, scikit-learn, NumPy, Pandas, and OpenCV
• Proven experience with Computer Vision architectures, including YOLO, SAM, U-Net, and other CNN-based models for real-time object detection, segmentation, and feature extraction
• Expertise in image classification using advanced CV models (e.g., ResNet, VGG, YOLOv5/v8, EfficientNet)
• Deep understanding and implementation experience of machine learning algorithms (supervised, unsupervised, and reinforcement learning) for classification, regression, and clustering
• Experience in Generative AI and Large Language Models (LLMs), including transformers, diffusion models, multimodal pipelines, and speech-to-text/NLP solutions
• Strong foundation in mathematics, statistics, data structures, algorithms, and optimization techniques
• Hands-on experience with RNNs/LSTMs, hybrid neural architectures, and temporal/spatiotemporal modeling
• Practical knowledge of Agentic AI systems (AI agents, multi-agent workflows, or autonomous agent design)
• GIS domain expertise – demonstrated ability to integrate AI/ML with geospatial datasets, satellite imagery, and spatial analytics to deliver practical solutions
• End-to-end experience in AI/ML solution lifecycle – from data preprocessing, model training, evaluation, deployment, and scaling
• Hands-on exposure to geospatial toolkits/libraries (e.g., GDAL, GeoPandas, QGIS, ArcGIS APIs) and their integration with ML workflows

Preferred Qualifications:

• Working knowledge of MLOps/LLMOps workflows for scalable, automated AI/ML deployments (CI/CD, containerization, orchestration)
• Experience with enterprise-grade data platforms (cloud-native, distributed systems) ensuring high performance and interoperability with AI workloads
• Practical exposure to geospatial feature extraction, change detection, and segmentation workflows
• Strong ability to communicate AI/GIS solutions, collaborate with cross-functional teams, and translate research into applied enterprise use cases