Full Stack Engineer MTS 2 – AIRI
On-siteToronto, Ontario, Canada
Job Summary
Design, develop, and optimize scalable AI systems using Generative AI, LLMs, retrieval-augmented generation, and agent-based architectures. Lead technical execution for major AI workstreams from design through production deployment, building agent-led user experiences that leverage task decomposition, memory, and orchestration. Partner with Product, Research, and Engineering teams to translate business needs into practical AI designs, owning architectural decisions for reliability, scalability, and cost efficiency. Contribute directly to backend services, model integration layers, APIs, and observability tooling while defining approaches for AI system evaluation and production feedback loops. Mentor engineers through hands-on technical guidance, code reviews, and collaborative problem-solving. Monitor and optimize AI systems in production for latency, quality, and responsible AI use.
Required Qualifications
- 8+ years of experience in software engineering, machine learning engineering, AI engineering, or related technical roles
- 4+ years of focused experience developing, deploying, and operating AI-centric or ML-powered systems in production environments
- 1–2+ years of experience leading technical initiatives, owning major engineering workstreams, mentoring engineers, or providing technical direction
- Hands-on experience building Generative AI, LLM, retrieval-augmented generation, conversational AI, or agent-led systems
- Experience taking AI-powered features or services from prototype to production with attention to maintainability, scalability, performance, reliability, and user impact
- Strong hands-on engineering skills, with the ability to contribute directly to complex system design and implementation
- Strong programming skills in Java or similar JVM languages, with working proficiency in Python and familiarity with ML frameworks such as PyTorch, Transformers, and scikit-learn
- Experience designing and operating production-grade backend systems, distributed services, APIs, or AI platforms that serve real-world user traffic
- Full-stack mindset with the ability or willingness to contribute to front-end development using modern web technologies
- Strong understanding of AI system evaluation, including offline evaluation, online experimentation, model behavior analysis, quality metrics, and feedback loops
- Hands-on experience with: Spring Framework or Spring Boot
- Hands-on experience with: Docker and Kubernetes
- Hands-on experience with: Large-scale data technologies such as Hadoop or Spark
- Hands-on experience with: Distributed systems and scalable backend services
- Hands-on experience with: Production monitoring, observability, and performance optimization
- Hands-on experience with: CI/CD, testing, deployment, and operational support practices
- Ability to evaluate technical tradeoffs and communicate complex AI concepts clearly to technical and non-technical collaborators
Desired Qualifications
- Experience with C++ or CUDA for performance-critical AI or ML components
- Familiarity with streaming data systems such as Kafka, Flink, Beam, or Storm
- Experience with vector databases, embeddings, semantic search, ranking systems, knowledge grounding, and retrieval-augmented generation
- Knowledge of agent orchestration frameworks, tool-use patterns, workflow automation, multi-modal models, or multi-agent systems
- Experience building internal AI platforms, reusable AI services, developer tools, or shared ML infrastructure
- Experience supporting high-traffic ecommerce, marketplace, search, personalization, recommendations, trust, ads, or customer-service AI systems
- Experience improving engineering practices through reusable patterns, documentation, testing frameworks, evaluation harnesses, or operational playbooks
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