Senior AI Engineer at ShyftLabs – Toronto, Kansas

About ShyftLabs

At ShyftLabs, we live and breathe data. Since 2020, we've been helping Fortune 500 companies unlock growth with cutting-edge digital solutions that transform industries and create measurable business impact. We're scaling globally with entities in Canada, the U.S., and India and we're looking for passionate problem-solvers who are ready to turn big ideas into real outcomes.

The Opportunity

ShyftLabs is looking for a Senior AI Engineer to sit at the intersection of AI/ML engineering and backend platform development. This isn't a research role, it's a hands-on engineering position where you'll design, build, and ship AI-powered features directly into production systems. You'll work closely with Product, Architecture, and cross-functional engineering teams to integrate intelligent capabilities into our next generation of data-driven platforms, while also contributing to the scalable backend infrastructure that powers them.

The ideal candidate combines deep AI/ML engineering experience with strong backend development skills, someone equally comfortable fine-tuning an LLM integration as they are designing a robust API or event-driven service.

• Architect and implement AI-powered features end-to-end, from model integration and prompt engineering to deployment, monitoring, and iteration

• Integrate LLM APIs, ML models, and intelligent automation pipelines into scalable, production-grade backend systems

• Fine-tune and optimize models for performance, reliability, and cost efficiency in live environments

• Design and build robust APIs, event-driven services, and third-party integrations that support AI-enabled workflows

• Collaborate with Product to translate business requirements into technical AI solutions, defining scope, complexity, and dependencies

• Build and maintain data pipelines and MLOps workflows to support model deployment and lifecycle management

• Contribute to system architecture decisions, integration patterns, and reusable AI frameworks across the platform

• Partner with data scientists and engineers to ensure smooth, scalable model deployments

• Produce clear technical documentation, architecture diagrams, data flows, API specs, and AI integration patterns

• Lead code reviews, enforce best practices in code quality and testing, and mentor engineers across teams

• Troubleshoot complex production issues across distributed and AI-integrated systems

• Stay ahead of industry trends in AI/ML tooling, frameworks, and practices

• 5+ years of experience building scalable, production-grade software systems

• 2+ years of hands-on AI/ML engineering experience, including deploying models into production environments

• Bachelor's degree in Computer Science, Data Science, AI, or a related field

• Strong expertise in Python, with hands-on experience using frameworks such as TensorFlow, PyTorch, or Scikit-learn

• Solid backend engineering background, API development, microservices, distributed systems, and event-driven architectures

• Experience integrating LLM APIs and building AI-driven features into web or platform applications

• Strong proficiency in JavaScript/TypeScript and Node.js, with exposure to GraphQL

• Deep experience building and consuming RESTful APIs and distributed services

• Proficiency with SQL and hands-on experience with cloud platforms, GCP or AWS (e.g., S3, Lambda, RDS, EC2)

• Familiarity with MLOps best practices and tools for model monitoring, versioning, and deployment

• Strong understanding of software design patterns, system architecture, and data flow design

• Excellent communication skills with the ability to articulate complex technical concepts clearly

• Experience working in Agile/Kanban environments with a strong grasp of the full SDLC

• Experience with data platforms, analytics systems, or ETL pipelines

• Familiarity with real-time data processing and messaging systems such as Kafka or SQS

• Exposure to tax, fintech, or compliance-related product development

• Experience designing multi-tenant or enterprise-grade SaaS platforms

• Background in integration-heavy environments such as ERP, financial systems, or external data providers