Design, build, and operate production-grade Generative and Agentic AI applications and services. Develop Python backend services, integrate cloud-hosted LLM APIs, implement RAG pipelines and vector search, build multi-agent systems, deploy on major cloud providers, ensure observability, security, and performance, and mentor junior engineers.
Background
We are looking for a Staff Generative AI Engineer to design, build, and ship production-grade Generative AI and Agentic AI applications that delivery business value across the organization. This role is focused on building AI applications and services at scale. You will be responsible for building robust, secure, and highly scalable systems that integrate with leading cloud-based AI services.
As a senior individual contributor, you will bring deep technical expertise, drive high-quality engineering practices, and serve as a mentor for junior to mid-level engineers. You will work closely with software engineers, data scientists, and product teams to translate business problems into production-grade AI applications and services.
Key Responsibilities
Application and Solution Engineering
- Design, build, and ship production-grade Generative and Agentic AI applications and services for internal and external users
- Develop high-quality backend services in Python, with strong software engineering rigor around testing, performance, and maintainability
- Champion reusability and abstraction in everything you build by designing and building modular, well-abstracted components and libraries
- Build multi-agent systems using frameworks such as LangChain, LangGraph, Claude Agent SDK and Google ADK
- Integrate with leading LLM and foundation model APIs, including Azure OpenAI, Google Vertex AI, and AWS Bedrock
- Design and implement Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking strategies, embeddings, vector search, and re-ranking
- Build clean, well-tested RESTful and/or gRPC APIs with a strong focus on reliability, security, and performance
- Implement observability, tracing, evaluation, guardrails for Generative and Agentic AI applications
- Deploy and operate services on major cloud providers (e.g., GCP, AWS, and Azure) leveraging managed services
- Contribute to platform architecture decisions and engineering best practices
- Take applications from prototype through production deployment, hardening, and ongoing operation
Mentorship and Team Development
- Mentor and coach junior and mid-level engineers through code reviews, architecture discussions, and pair programming
- Foster a culture of engineering excellence, knowledge sharing, and continuous improvement
- Participate in technical design reviews and contribute to the professional growth of team members
Required Qualifications
- 10-15 years of professional software engineering experience with at least 3-5 years of experience building AI/ML software products
- Bachelor’s degree in Computer Science or a related field (Master’s degree preferred)
- Strong proficiency in Python, with deep software engineering fundamentals (abstraction, modularity, system design, testing, performance)
- Hands-on experience building and shipping Generative and Agentic AI applications, including LLM integration, prompt engineering, and/or agentic workflows
- Practical experience integrating cloud-hosted LLM APIs such as Azure OpenAI, Vertex AI, and/or AWS Bedrock
- Experience with agent frameworks (e.g., LangChain, LangGraph, Google ADK, Claude Agent SDK) and vector databases (e.g., Pinecone, Weaviate, pgvector, Open Search, AlloyDB)
- Hands-on experience with Google Cloud Platform (GCP), Amazon Web Services (AWS), or Azure
- Strong understanding of API design, distributed systems, and cloud-native architecture
- Proven track record of taking systems from design through production deployment and operation
Preferred Qualifications
- Experience with containerization and orchestration (Docker, Kubernetes)
- Knowledge of Generative AI Risk Management frameworks (NIST RFM)
- Experience supporting developer platforms or internal tooling
- Experience writing design documents or helping define engineering standards
What you need to know about the Toronto Tech Scene
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