6 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
Categories
About ShyftLabs
ShyftLabs builds and runs enterprise data and AI platforms, combining data engineering, analytics, and responsible AI controls with consulting and managed delivery. It designs governed data infrastructure, customer and retail analytics, and omnichannel marketing data products for large organizations. Founded in 2018 and headquartered in Toronto, the privately held firm serves global clients and staffs teams across Toronto, New York, Dubai, and New Delhi.