AI Engineer/Cloud Engineer
Job Description & How to Apply Below
# AI Engineer / Cloud Engineer Sage Beans RPOFull Timemid Hybrid Red Deer, Alberta, CAPosted Yesterday##
Role Overview Sage Beans RPO is hiring a mid-level AI Engineer / Cloud Engineer. This is a full-time hybrid role, based in Red Deer. Part of Sage Beans RPO's Security hiring, posted yesterday. Full responsibilities, required qualifications, and the apply link are listed in the description below.## Salary Context Salary is not disclosed in this posting. Market median for Mid-level Security roles is $87k-$125k (based on 288 comparable listings).
Many employers share specifics during the interview process or after an initial screen.## Resume Keywords to Include Make sure these keywords appear in your resume to improve ATS scoring
Python Type Script Node .jsAWSAzure Git Hub Actions Git Hub RESTSign up free to auto-tailor your resume with all these keywords and get a higher ATS score##
Job Description
AI Engineer / Cloud Engineer Core Stack: AWS Bedrock Agent Core
* Azure AI Foundry
* MCP Governance Function:
Foundations / Infrastructure & Operations Level: Senior / Staff Type:
Full-time
Location:
Remote / Hybrid
Reports to:
Director, AI Architecture### About the Role The Foundations team serves as the enterprise AI governance control plane for Infrastructure & Operations (I&O), responsible for the infrastructure, observability, security, and policy layer across a multi-cloud AI agent ecosystem.
We are seeking a senior AI/Cloud Engineer to design, build, and operate production-grade AI agent infrastructure across Amazon Web Services Bedrock Agent Core and Microsoft AI Foundry, with deep integration across MCP (Model Context Protocol) connectors, LLM gateways, and enterprise data systems.
This role sits at the intersection of AI platform engineering, cloud infrastructure, governance, and enterprise security. You will partner closely with the I&O Architecture Engineering team to ensure AI agents are observable, measurable, secure, and fully governed across the enterprise estate.
Key Responsibilities
1. Agent Infrastructure & Platform Engineering Design and deploy production AI agent workloads on AWS Bedrock Agent Core, including runtime configuration, memory stores, and Data Dog observability instrumentation. Build and maintain Azure AI Foundry agent pipelines with Application Insights telemetry, Azure APIM-based token attribution, and Azure Monitor integration for safety and red-teaming signals. Architect MCP connector infrastructure, including tool-call routing, RBAC enforcement, OAuth2 / Entra , and end-to-end audit logging.
Maintain and evolve the enterprise LLM gateway as the centralized routing, policy enforcement, and instrumentation layer across Bedrock, Azure OpenAI, and Claude-based endpoints.
2. Governance, Security & Observability Instrument agent systems to capture Tier 1 audit KPIs such as tool-call completeness, policy violations, RBAC coverage, and authentication failure rates aligned with compliance requirements. Define per-connector security policies ensuring Finance, HR, Legal, and Client data systems remain fully governed and least-privileged. Build unified observability dashboards across Cloud Watch, Azure Monitor, and Data Dog for AI system health and executive reporting.
Participate in CDR (Critical Design Review) processes for all AI agent deployments, ensuring adherence to reliability, security, and observability standards. Design SOC 2-aligned audit log pipelines for all agent tool-calls to support compliance and forensic traceability.
3. Integration & Platform Interoperability Integrate AI agent systems with enterprise platforms such as Service Now, Data Dog, Apptio/Cloudability, and internal data platforms via MCP connectors and REST APIs. Support CI/CD automation for AI agents using Git Hub Actions, including environment promotion, rollback strategies, and pipeline replay mechanisms.
4. AI Quality & Evaluation Define and instrument agent KPIs including task completion rate, hallucinated tool-call detection, escalation rate, and context efficiency metrics. Leverage AWS Agent Core evaluation frameworks and Azure AI Foundry evaluation tooling to assess groundedness, safety, tool accuracy, and reliability. Build golden dataset regression suites to detect performance degradation across model updates, prompt changes, and connector modifications.
Required Qualifications Platform Experience (Must Have)
AWS Bedrock Agent Core (production workloads) Azure AI Foundry (agent pipelines & evaluation systems) AWS Lambda / EKS Azure API Management (APIM) Cloud Watch (metrics, logs, traces) Azure Monitor + Application Insights LLM Gateway architecture experience MCP (Model Context Protocol) servers OAuth2 / Entra / IAM-based credential scoping
Engineering Skills5+ years software/platform engineering experience; 2+ years in AI/ML infrastructure or LLM-based systems Strong proficiency in Python;
Type Script/Node.js preferred for orchestration layers
Experience with REST APIs, async processing, and event-driven…
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