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 thepanys 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: 35 years of professional software engineering experience, with at least 12 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: Robust 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 & Dev

Ops: 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 Web

Sockets) for real-time, low-latency chatbot interactions.

Understanding of data privacy, security guardrails, and PII handling in generative AI applications.

📌 AI Engineer (North York)
🏢 Hays
📍 North York