
Enterprise AI/ML Solutions Developer
24 hours ago
We are seeking an experienced software developer with expertise in designing, developing, and deploying AI/ML solutions at an enterprise level.
The ideal candidate will have hands-on experience in Computer Vision, Deep Learning, Generative AI, and GIS-based AI applications, along with strong programming, analytical, and solution-building skills.
Key Responsibilities:- Design, develop, and deploy AI-powered GIS platforms, multimodal and geospatially aware models, and real-time feature detection systems.
- Contribute to the end-to-end lifecycle of enterprise-grade AI/ML solutions.
The ideal candidate will have:
- 3-5 years of hands-on experience in implementing AI/ML solutions in enterprise or 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.
In addition, the ideal candidate will have a strong ability to communicate AI/GIS solutions, collaborate with cross-functional teams, and translate research into applied enterprise use cases.
Preferred QualificationsWorking 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.
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