Data/Machine Learning Infrastructure Engineer

Wavelo is a SaaS business on a mission to make telecoms a breeze.

We provide flexible software that modernizes how communication service providers (CSPs) do business, helping them drive more value, focus on customer experience, and scale their operations faster. Wavelo builds cutting-edge software for the telecom industry, specializing in billing, orchestration, and provisioning solutions. Our mission is to empower telecom providers with robust tools that streamline operations and drive impactful decisions. We are now expanding our AI capabilities to derive deep insights from our event-stream data, enabling us to take meaningful actions that enhance our products and customer experience.

What's New at Tucows

As part of Tucows (NASDAQ:TCX, TSX:TC)—one of the world’s largest Internet services companies—Wavelo is backed by outstanding resources and talent. We embrace a people-first philosophy that is rooted in respect, trust, and flexibility. We believe that whatever works for our employees is what works best for us. It’s also why the majority of our roles are remote-first, meaning you can work from anywhere you can connect to the Internet!

The work we do genuinely changes lives. If this sounds exciting, we’d love to hear from you!

Position Overview: As a Data/AI Infrastructure Engineer at Wavelo, you will play a pivotal role in building and optimizing the infrastructure necessary to process events at scale, enabling machine and deep learning capabilities for real-time insights. Your work will enable real-time insights and drive automation within our billing, orchestration, and provisioning systems. You will collaborate with cross-functional teams to implement, fine-tune, and optimize AI solutions for seamless data flow and actionable insights.

Key Responsibilities:

• AI Solution Deployment: Deploy AI and machine learning models within Wavelo’s production environment, aligning with our software architecture to drive actionable insights from our telecom event streams.
• Kafka Integration: Develop and maintain Kafka pipelines to support real-time data processing, facilitating the flow of critical insights and enabling swift actions on event-stream data.
• Model Training and Tuning: Manage model training, tuning, and retraining workflows, ensuring that models are optimized to capture actionable insights from our event streams.
• Cross-Functional Collaboration: Work with data engineers, product teams, and other stakeholders to align on data needs, model requirements, and integration objectives.
• Monitoring and Optimization: Develop and implement monitoring solutions for model performance within the event-stream environment, identifying and resolving issues proactively.
• Documentation and Knowledge Sharing: Document processes, model configurations, and best practices to promote knowledge sharing and continuous improvement across Wavelo.

Qualifications:

The ideal candidate is hands-on and has extensive experience with machine learning, deep learning, and AI data processing clusters, including Apache Spark and other relevant technologies. They are deeply passionate about AI and have exposure to training models either locally or through cloud platforms like Google Vertex AI, AWS SageMaker, or Azure ML.

• Experience: 3+ years in AI/ML deployment, solutions engineering, or similar roles, with hands-on experience in Kafka and event-stream integration.
• Technical Skills:
• Proficiency with Apache Kafka for setting up, managing, and scaling event-stream pipelines.
• Familiarity with LangChain and related AI/ML frameworks (TensorFlow, PyTorch, scikit-learn) and cloud environments (AWS, Azure, Google Cloud).
• Experience in RESTful APIs, microservices, and containerization technologies such as Docker and Kubernetes.
• Solid grounding in data engineering practices, with a strong emphasis on handling and cleaning messy, unstructured data to ensure quality for downstream machine learning tasks. Experience in feature engineering and continuous model improvement is essential, as this role will involve significant effort in preparing complex data for large-scale processing.
• Problem-Solving Skills: Analytical and proactive approach to resolving integration and performance challenges.
• Communication Skills: Strong ability to convey technical concepts to both technical and non-technical stakeholders, with a collaborative and team-oriented mindset.

Preferred Qualifications:

• Experience