Machine Learning Engineer
Bengaluru, India
2d ago

Company Information

Mixing technology, data, and first-in-class innovation, EagleView® is not only leading the property data analytics market, but also changing lives along the way.

Come join us and make great things happen!

EagleView is a fast-growing technology company driving game changing innovation in multibillion-dollar markets such as property insurance, energy, construction, and government.

Leveraging 17 years of the most advanced aerial imaging technology in the world, along with the most recent advances in machine learning and AI, EagleView is fundamentally transforming how our customers do business.

  • At EagleView, we believe that making our culture engaging and empowering are keys to success. Our social, athletic, and wellness opportunities are plentiful;
  • and the growth, education, and potential of employees is a top priority, making EagleView a Best Place to Work for more than five years running.

    Responsibilities :

  • Work closely with the engineering team helping them continuously deploy machine learning
  • models to production

  • Analyse and improve on current data and model development and deployment pipelines
  • Keeping abreast of state of the art deep learning frameworks predominantly focused towards
  • inference e.g. TensorRT, OpenVINO, RapidAI

  • Maintain and optimise model inference performance on both CPU and GPU
  • Develop and train deep learning models
  • Skills & Requirements

    Professional Experience : 4-7 years

    Educational Background : B.Tech, M.Tech (Optional), Computer Science

    Required Skills :

  • 2-3 years of experience in Computer Vision and Deep Learning
  • Proficient in writing production grade code in python. Familiarity with numpy and OpenCV.
  • Knowledge of Machine Learning frameworks e.g. PyTorch, Tensorflow, MxNet, ONNX
  • Prior experience deploying deep learning models to production
  • Proficiency in git.
  • Familiarity with Docker and Docker Compose, Flask, FastAPI
  • Familiarity with kubernetes is a preferred.
  • Comfortable in training and deploying models in AWS ecosystem.
  • Understanding of Deep learning fundamentals in areas like image classification, object
  • detection and instance segmentation.

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