Software Engineer (Machine Learning)

We are looking for an experienced Software Engineer – Machine Learning with 3–5 years of proven expertise in designing, developing, and deploying AI/ML solutions at an enterprise level. The ideal candidate will bring hands-on experience in Computer Vision, Deep Learning, Generative AI, and GIS-based AI applications, along with strong programming, analytical, and solution-building skills. In this role, you will contribute to the development of AI-powered GIS platforms, multimodal and geospatially aware models, and real-time feature detection systems, playing a key role in the end-to-end lifecycle of enterprise-grade AI/ML solutions.

Must 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.

Should Have

-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.

Could Have

-Certifications in AI/ML, GIS, or cloud platforms (AWS, Azure, GCP).

-Familiarity with scientific computing libraries (SciPy, Theano, Julia ecosystem).

-Experience contributing to AI/GIS research publications, open-source projects, or innovation programs.

-Knowledge of emerging paradigms such as federated learning, multi-agent systems, spatial AI, or Responsible AI practices.