5 months ago
Toronto, CanadaSenior
Responsibilities
- Architect and implement end-to-end AI-powered features, including model integration, prompt engineering, deployment, monitoring, and iteration.
- Integrate LLM APIs, ML models, and intelligent automation pipelines into scalable backend systems.
- Fine-tune and optimize models for performance, reliability, and cost efficiency in production.
- Design and build APIs, event-driven services, third-party integrations, and AI-enabled workflows.
- Collaborate with Product and engineering teams to define technical solutions, scope, complexity, and dependencies.
- Build and maintain data pipelines and MLOps workflows for model deployment and lifecycle management.
- Contribute to architecture decisions, integration patterns, reusable AI frameworks, and scalable model deployments.
- Create technical documentation, architecture diagrams, data flows, API specifications, and AI integration patterns.
- Lead code reviews, promote code quality and testing practices, mentor engineers, and troubleshoot distributed production systems.
Requirements
- 5+ years of experience building scalable, production-grade software systems.
- 2+ years of hands-on AI/ML engineering experience, including deploying models into production.
- Bachelor’s degree in Computer Science, Data Science, AI, or a related field.
- Strong Python skills and hands-on experience with TensorFlow, PyTorch, or Scikit-learn.
- Strong backend engineering experience with API development, microservices, distributed systems, and event-driven architectures.
- Experience integrating LLM APIs and building AI-driven features for web or platform applications.
- Proficiency with JavaScript/TypeScript and Node.js, with exposure to GraphQL.
- Deep experience building and consuming RESTful APIs and distributed services.
- Proficiency with SQL and cloud platforms such as GCP or AWS, including services such as S3, Lambda, RDS, and EC2.
- Familiarity with MLOps practices and tools for model monitoring, versioning, and deployment.
- Strong understanding of software design patterns, system architecture, and data flow design.
- Experience working in Agile/Kanban environments and understanding of the full software development lifecycle.
- Preferred experience includes data platforms, analytics systems, ETL pipelines, real-time processing, Kafka or SQS, tax or fintech products, compliance-related development, multi-tenant SaaS, ERP, financial systems, or external data providers.
Benefits
- Hybrid work arrangement with three days per week in the Toronto office.
- Downtown Toronto office location.
- 100% company-paid health, dental, and vision insurance premiums for employees and dependents, eligible from day one.
- Learning and development resources.
- Equal-opportunity and inclusive workplace with interview accommodations available upon request.
Tech Stack
Apache KafkaAWSGoogle Cloud PlatformGraphQLJavaScriptNode.jsPythonPyTorchscikit-learnSQLTensorFlowTypeScript
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