Full stack Engineer MTS 1 – AIRI

eBay•Toronto, ON

2d•CA$142,400 - CA$190,100

About The Position

eBay is seeking a skilled, motivated, and hands-on Full Stack Engineer (MTS 1) to join our AIRI division. This is an opportunity to build production-quality AI features, services, integrations, and platform components that power intelligent experiences across one of the world’s largest ecommerce platforms. This is an individual contributor role for a strong senior engineer who enjoys hands-on building, solving practical AI engineering problems, and turning ideas into reliable production systems. We are looking for someone with a full-stack mindset: strong backend engineering depth, comfort working across the stack, and the ability or willingness to contribute to front-end experiences as needed, without requiring expertise in any specific front-end framework. In this role, you will partner closely with senior technical leads, Product, Research, Data Engineering, and Software Engineering teams. You will be expected to demonstrate technical ownership, contribute to design discussions and reviews, support code reviews, and mentor less experienced engineers where appropriate. As a Full Stack Engineer (MTS 1), you will contribute across the AI development lifecycle, including experimentation, prototyping, implementation, evaluation, production deployment, monitoring, and iteration. Your work will span Generative AI applications, LLM integrations, conversational AI, retrieval-augmented generation, agent workflows, AI evaluation, and scalable backend services. You will help build systems that are reliable, maintainable, measurable, and ready for production use.

Requirements

5+ years of experience in software engineering, machine learning engineering, AI engineering, or related technical roles.
2+ years of experience developing, deploying, or supporting AI-centric, ML-powered, data-driven, or intelligent software systems.
Hands-on experience building or integrating Generative AI, LLM-based applications, retrieval-augmented generation, conversational AI, or agent workflows.
Strong programming skills in Java or similar JVM languages, with working proficiency in Python.
Experience with ML or AI development tools and frameworks such as PyTorch, Transformers, scikit-learn, LangChain-style orchestration, or similar technologies.
Experience building production backend services, APIs, distributed systems, or data-driven applications.
Full-stack mindset with the ability or willingness to contribute to front-end development using modern web technologies; expertise in a specific front-end framework is not required.
Working knowledge of AI system evaluation, including quality metrics, test sets, experimentation, model behavior analysis, and production feedback loops.
Hands-on experience with: Spring Framework or Spring Boot
Docker and Kubernetes
RESTful APIs and scalable backend services
Large-scale data technologies such as Hadoop, Spark, or equivalent systems
CI/CD, automated testing, monitoring, and observability
Ability to debug production issues, analyze system behavior, and improve reliability, latency, and maintainability.
Strong communication skills and the ability to work effectively with engineering, research, product, and data partners.

Nice To Haves

Experience with agent-led systems, tool-using agents, autonomous workflows, task-planning systems, or multi-agent orchestration.
Experience with vector databases, embeddings, semantic search, retrieval-augmented generation, and knowledge-grounded AI systems.
Familiarity with streaming data systems such as Kafka, Flink, Beam, or Storm.
Experience with conversational AI concepts such as intents, entities, dialogue flows, actions, and user journeys.
Experience building high-traffic ecommerce, marketplace, search, personalization, recommendations, trust, ads, or customer-support systems.
Experience contributing to internal platforms, shared AI infrastructure, developer tools, evaluation frameworks, or reusable services.
Familiarity with responsible AI practices, safety evaluation, model monitoring, and production governance.

Responsibilities

Design, develop, test, and deploy AI-powered features, services, APIs, and platform components.
Build and integrate systems using Generative AI, LLMs, retrieval-augmented generation, conversational AI, and agent-based patterns.
Implement components for agent-led workflows, including tool use, memory, retrieval, task planning, and response generation.
Collaborate with engineers and technical leads on system design, implementation plans, technical tradeoffs, and operational readiness.
Partner with Product, Research, Data Engineering, and Software Engineering teams to understand requirements and deliver practical AI solutions.
Build backend services, orchestration layers, evaluation pipelines, model integration services, and observability features.
Participate in design reviews, code reviews, debugging sessions, and production readiness discussions.
Contribute to reusable components, APIs, shared libraries, documentation, and engineering guidelines for GenAI applications.
Implement evaluation and experimentation workflows to measure AI system quality, model behavior, latency, reliability, and user impact.
Monitor and troubleshoot production AI systems, including latency, errors, quality regressions, cost, and operational issues.
Improve system quality through testing, documentation, performance tuning, observability, and maintainability improvements.
Stay current with practical developments in AI, LLMs, agent frameworks, retrieval systems, and ML engineering tools.
Mentor junior engineers through code reviews, implementation guidance, documentation, and knowledge sharing.