I am hiring an Intermediate AI Engineer on a 6-month contract with a high probability of further extension on behalf of a well-established organization in Toronto.
In this role, you will help build and scale thepany’s next-generation Agentic AI chatbots and systems while contributing to a growing portfolio of GenAI applications designed to solve real-world problems. You will bridge the gap between ambiguous LLM capabilities and deterministic, high-performance production software, will be designing autonomous agent workflows using Google ADK, engineering dynamic context pipelines, and deploying robust backend services.
Key Responsibilities:
Agentic AI & GenAI Development
• Agent Orchestration: Design and implement intelligent agent workflows (e.g., tool-calling, planning, multi-agent collaboration, and self-reflection) primarily using Google ADK (Agent Development Kit).
• Context Engineering & RAG: Build and optimize end-to-end RAG pipelines. Implement advanced techniques such as smart document chunking, hybrid search, re-ranking, and dynamic context window management.
• Prompt Engineering: Develop, test, and version-controlplex system prompts to ensure structured, reliable, and hallucination-free outputs from foundation models.
• Evaluation: Familiarity with LLM evaluation frameworks for systematically measuring hallucination rates, precision, and recall.
• Observability: Strong understanding of Multi-Agents Observability to debug & improve accuracy/latency
Backend Engineering
• API Development: Architect and develop high-performance, asynchronous microservices and RESTful APIs using FastAPI to connect our AI agents with the front-end and internal systems. • System Integration: Safely and efficiently integrate external tools, enterprise APIs, and data sources into the agentic workflows, so our assistants can take real-world actions.
Cloud Infrastructure & Database Management
• Vector & Relational Databases: Set up, manage, and optimize vector search capabilities using Cloud SQL for PostgreSQL (pgvector) to handle both structured relational data and high-dimensional semantic retrieval. • Deployment & Scaling: Containerize AI applications using Docker and deploy them onto Google Kubernetes Engine (GKE). • CI/CD & Version Control: Maintain clean code repositories using Git to streamline testing and deployment.
Required Qualifications
• Experience: 3–5 years of professional software engineering experience, with at least 1–2 years actively building and deploying production LLM or machine learning applications.
• Programming: Expert-level proficiency in Python (including a strong understanding of AsyncIO).
• Web Frameworks: Strong hands-on experience building robust backend services with FastAPI.
• AI/LLM Ecosystem: Deep understanding of RAG architectures, vector embeddings, semantic search, and prompt engineering. Active experience with Google ADK is highly preferred.
• Database Experience: Strong SQL skills and experience managing embeddings using PostgreSQL and the pgvector extension.
• Cloud & DevOps: Proven experience with GCP, containerization (Docker), and orchestration (Kubernetes/GKE).
• Software Engineering Fundamentals: Solid grasp of Git workflows, unit/integration testing (e.g., pytest), API design, and distributed system architecture.
Preferred Qualifications (Nice-to-Haves)
• Familiarity with other GenAI orchestration frameworks to bring diverse architectural perspectives and best practices.
• Experience handling streaming responses (Server-Sent Events or WebSockets) for real-time, low-latency chatbot interactions.
• Understanding of data privacy, security guardrails, and PII handling in generative AI applications.
AI Engineer • North York
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