Senior AI Engineer
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.
What You'll Do
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
What You Bring
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
Nice to Have
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