Senior Machine Learning Engineer

1 week ago

Thoothukudi, Tamil Nadu, India beBeeArtificial Full time ₹ 1,50,00,000 - ₹ 2,00,00,000

Job Title: Software Developer

Job Summary:

We are seeking an experienced software developer with 3-5 years of hands-on experience in implementing AI/ML solutions in enterprise or large-scale production environments.

Key Responsibilities:

  • Design, develop, and deploy AI/ML models at an enterprise level.
  • Contribute to the development of GIS platforms, multimodal and geospatially aware models, and real-time feature detection systems.
  • Collaborate with cross-functional teams to translate research into applied enterprise use cases.

Requirements:

  • 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, 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.

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