Our team and what we'll accomplish together
Join TELUS Technology's Network Automation and Intelligence team, where we're revolutionizing how Canada's leading telecommunications network operates. We're a dynamic group of software developers and network specialists who are building the intelligent, self-healing network of tomorrow. Our mission? To leverage cutting-edge AI and machine learning to predict network issues before they happen, optimize performance in real-time, and deliver exceptional experiences to millions of Canadians.
As part of our team, you'll be at the intersection of telecommunications and artificial intelligence, working on problems that directly impact network reliability, efficiency, and innovation. We're not just maintaining infrastructure—we're building intelligent systems that learn, adapt, and evolve. Your work will power everything from predictive maintenance algorithms that prevent outages to ML models that optimize network traffic across our national infrastructure. If you're passionate about applying AI to solve real-world problems at massive scale, this is where you belong.
What you'll do
Design and develop end-to-end ML/AI pipelines that process terabytes of network telemetry data to predict failures, optimize performance, and automate network operations
Build and optimize robust ETL/ELT workflows using GCP tools (Dataflow, Dataproc, Cloud Composer) to ingest, transform, and prepare network data from diverse sources for ML model training and inference
Implement and maintain AI integration protocols including MCP (Model Context Protocol) servers to enable seamless communication between AI models and network data sources, tools, and external systems
Collaborate with network engineering teams, data scientists, and platform architects to identify automation opportunities and translate business requirements into scalable AI solutions
Create and deploy production-grade machine learning models for network anomaly detection, capacity forecasting, and intelligent routing optimization
Develop standardized protocols and APIs for AI model serving, ensuring efficient context sharing and tool integration across distributed network automation systems
Partner with DevOps and MLOps teams to implement CI/CD pipelines for model deployment, monitoring, and continuous improvement in GCP environments
Build data quality frameworks and monitoring systems to ensure the reliability and accuracy of data flowing through our ML pipelines
Drive the adoption of best practices in feature engineering, model versioning, experiment tracking, and protocol-based AI integrations across the network automation domain
Analyze complex network datasets using advanced statistical methods and ML techniques to uncover insights that inform strategic network planning decisions
Support the migration and modernization of legacy data systems to cloud-native architectures, ensuring scalability and performance
Influence technical strategy by staying current with emerging AI/ML technologies, protocols, and recommending innovative approaches to network automation challenges
What you bring
Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Engineering, or related technical field
3+ years of hands-on experience developing and deploying machine learning models in production environments
Deep understanding of ML/AI concepts including supervised/unsupervised learning, deep learning, time-series forecasting, and anomaly detection
Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost)
Proven experience designing and implementing ETL/ELT pipelines for large-scale data processing
Solid expertise with GCP services including BigQuery, Cloud Storage, Vertex AI, Dataflow, Pub/Sub, and Cloud Functions
Experience with SQL and NoSQL databases (BigQuery, Cloud SQL, Firestore, or similar)
Strong understanding of data modeling, data warehousing concepts, and dimensional modeling
Knowledge of API design, RESTful services, and communication protocols for AI/ML systems
Demonstrated ability to write clean, maintainable, production-quality code with proper testing and documentation
Excellent problem-solving skills with the ability to translate complex business problems into technical solutions
Great-to-haves
Experience implementing MCP (Model Context Protocol) servers or similar AI integration protocols for context management and tool orchestration
Familiarity with telecommunications networks, network protocols (SNMP, NetFlow, gRPC), or infrastructure monitoring systems
Knowledge of MLOps practices and tools (Kubeflow, MLflow, Airflow) for model lifecycle management and real-time streaming data processing in network environments
Location: Anywhere in Canada #LI-Remote
