Principal AI Developer
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- Waterloo, ON
- Permanent
- Full-time
- AI Agent Research: Evaluate emerging agent frameworks, model capabilities, software patterns, and research trends to guide strategic adoption.
- AI Agent Architecture: Define reference agent architectures, design patterns, and best practices covering LLM selection, reasoning, planning, prompting, state, memory, tool orchestration, identity, access, security, observability, evaluation, and autonomy.
- AI Agent Design: Translate business processes, rules, constraints, roles, performance thresholds, facts, and artifacts into agent requirements, patterns, and flows.
- AI Agent Development: Design and implement complex AI agents (primarily Python) integrated with enterprise systems, APIs, and data platforms.
- AI Agent Testing & Evaluation: Perform functional and safety testing, regression testing, agent response evaluation, human-in-the-loop feedback, and automated quality metrics.
- AI Agent Deployment & Governance: Automate deployment, monitoring, and governance of AI agents in scalable cloud production environments.
- AI Agent Troubleshooting: Rapidly diagnose and resolve issues related to agent performance, behavior, results, safety, and security.
- AI Data & ML Models: Acquire data via SQL and REST APIs (including MCP Server), and use, enhance, or develop ML models to solve business problems.
- Experience: 2+ years in AI agent design and development (end-to-end agentic systems) and 5+ years in Software Engineering, DevOps, SRE, or Cloud Engineering.
- Education: Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or similar required; Master’s in AI or related field highly desirable.
- Agent Expertise: Strong experience with AI agent frameworks (Google Agent Development Kit or similar) and agent design, development, and deployment (ITSM use cases desirable).
- Programming & Data: Strong skills in Python, Bash, YAML, JSON; proficient in SQL and REST API–based data extraction.
- AI/ML Platforms: Proficient with Vertex AI, SageMaker, Azure ML, and developing or integrating ML models.
- CI/CD & Automation: Proficient with CI/CD tools (GitLab CI/CD, Flux, Argo Workflows) and infrastructure automation (Terraform, Helm, Ansible).
- Cloud & Runtime: Proficient with AWS, GCP, or Azure; container orchestration (EKS/GKE/AKS, Anthos/Google Distributed Cloud) and model serving (VLLM, TorchServe).
- Ecosystem & Governance: Proficient with MCP Server Protocol (MCP) and MCP Server development; exposure to graph databases, monitoring/logging (Prometheus, Grafana, ELK, CloudWatch), data/model versioning, experiment tracking, and AI/ML security, compliance, and governance.