Computer Vision Engineer

1 month ago

Bengaluru, India KaleidEO Full time

About KaleidEO


KaleidEO, a subsidiary of SatSure, is investing in launching its own fleet of earth observation satellites. We are seeking an Image Processing wizard to own and nurture the logical pipeline that delivers the best geospatial images and products. SatSure is a deep tech, decision Intelligence company that works primarily at the nexus of agriculture, infrastructure, and climate action creating an impact for the other millions, focusing on the developing world. We want to make insights from earth observation data accessible to all. The synergy of KaleidEO and SatSure aims to bring a new dimension to the earth observation industry by being the only full-stack company from India, to have satellites in space to deliver insights on the ground. If you are interested in working in a space tech analytics domain driven by cutting-edge technology on the edge as well as on the ground for solving complex data problems using Multi-Sensor Earth Observation data you will have the freedom to work on innovative ideas and be creative with no hierarchies, KaleidEO us the place for you.


Role:

  • Develop innovative solutions and prototypes using High-Resolution Earth Observation datasets for geospatial intelligence applications catering to business needs.
  • Lead the full R & D life cycle by taking end-to-end ownership of the completion of the work by meeting desired success criteria.
  • Design and implement state-of-the-art deep learning and machine learning algorithms in the field of computer vision for high-resolution earth observation dataset analytics along with creating the artifacts related to model validation, process result evaluation, presentations, and publishing the new research.
  • Collaborate effectively with the technical and non-technical team members to achieve the desired benchmarks within the given time frame. Also, demonstrate effective and empathetic communication skills to facilitate better decision-making across multiple stakeholders.



Requirements

Experience:

  • A minimum of 4 years of industrial experience is required.
  • Excellent coding skills in Python and C++.
  • Good hands-on experience with Computer Vision libraries such as OpenCV, Scikit-Image, and Boost.
  • Knowledge of Geospatial libraries & APIs, including GDAL, RasterIO, GeoPandas, and Shapely, is essential.
  • Experience with Cloud Platforms especially with Amazon AWS and on-prem hardware is must to have.
  • Familiarity with DevOps tools like Docker, Git, and Kubernetes is a plus.
  • Strong foundational understanding and experience with various machine learning algorithms and feature reduction techniques.
  • Prior to Deep Learning expertise for semantic and instance segmentation, object detection is a must to have.
  • Hands-on experience and knowledge of Deep Learning libraries, algorithms such as TensorFlow & Keras, PyTorch, FastAI, U-Net, YOLO >= 5, Mask-R CNN, and Segment Anything (SAM) will be considered as a strong asset in the candidate.
  • Familiarity with popular satellite imaging sensors, such as Optical (Maxar, Airbus, Planet, Satellogic, BlackSky, Sentinel-2) and SAR (Umbra, Capella, Sentinel-1) will be considered as a strong asset in the candidate. Strong understanding and familiarity with data science lifecycle practices.
  • Knowledge of creating a full stack application using the MLOps framework will be considered an asset in the candidate.


Competencies:

  • A proven ability to learn new tools and technologies quickly.
  • Ability to develop novel solutions rather than look for readily available options.
  • An ability to translate complex topics and tools into easy-to-understand concepts in order to explain the impact of developed models to stakeholders and peers.
  • Excellent debugging and critical thinking skills.
  • Excellent analytical and problem-solving skills.
  • Ability to work in a fast-paced, team-based environment.


Educational Qualifications:

  • Master’s degree or Ph.D. degree in Mathematics or Computer Science, coupled with prior experience in remote sensing and computer vision.

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