Senior Data Scientist to develop and deploy AI/ML-driven solutions across IT/OT infrastructure
S.i. Systèmes
Calgary, AB-
Nombre de poste(s) à combler : 1
- Salaire À discuter
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Emploi Contrat
- Publié le 5 juillet 2025
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Date d'entrée en fonction : 1 poste à combler dès que possible
Description
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
Exigences
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