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Senior Data Scientist to develop and deploy AI/ML-driven solutions across IT/OT infrastructure

Calgary, AB
  • Nombre de poste(s) à combler : 1

  • À discuter
  • Emploi Contrat

  • Date d'entrée en fonction : 1 poste à combler dès que possible

Our large, enterprise client is seeking a Senior Data Scientist to develop and deploy AI/ML-driven solutions across IT/OT infrastructure. This is an initial 1-year contract with a possibility of extension.


Must-Haves:

  • 5+ years as a Data Scientist, with a strong background in machine learning, deep learning, and statistical modeling
  • Experience developing and deploying AI/ML-driven solutions
  • Expertise with Python and C++ programming languages
  • Experience with computer vision ecosystems and ML toolsets: PyTorch, OpenCV (with CUDA acceleration), GStreamer, DeepStream, CuPy
  • Expertise in Python concurrency, including the CPython Global Interpreter Lock (GIL) and its trade-offs
  • Expertise developing in a Unix OS environment
  • Proficiency with CLI tooling, environment bootstrapping/maintenance, compiling open-source from source, Unix automation, and containerization (Docker, Kubernetes, etc.)


Nice-to-Haves:

  • Experience working with industrial data, IoT, and automation technologies
  • Experience integrating AI models with OT/IT infrastructure


Responsibilities Overview:

  • Build high-performance visual analytics systems for industrial use cases (e.g. anomaly detection on conveyor belts)
  • Implement low-latency on-GPU pipelines for frame decoding and inference (GStreamer, DeepStream)
  • Use profiling tools (like NVIDIA Nsight) to diagnose and optimize performance bottlenecks
  • Deploy AI models into production environments tied to IT/OT operations-supporting automation, predictive maintenance, and digital transformation
  • Work collaboratively across teams to translate business challenges into data-driven solutions
  • Ensure AI/ML integrations meet performance, reliability, and cybersecurity requirements
  • Communicate technical findings to both technical and non-technical stakeholders
  • Document code, models, processes, and best practices for reproducibility and scale
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