Senior AI Engineer
New
Fully remote work within CanadaFull-TimeSenior
SalaryCompetitive compensation aligned with experience and market standards, including base salary and equity (RSUs)
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Job Details
- Experience
- 8+ years of software engineering experience
- Required Skills
- Node.jsPythonGCPJavascriptCI/CDPrompt EngineeringLLMDistributed Systems
Requirements
- 8+ years of software engineering experience with strong expertise in backend systems, data engineering, or distributed systems.
- 2+ years of hands-on experience building production AI/LLM-powered systems beyond prototypes.
- Strong proficiency in Python and JavaScript/Node.js with solid engineering practices (testing, CI/CD, Git workflows).
- Deep experience with LLM frameworks and patterns including prompt engineering, tool use/function calling, RAG, and evaluation techniques.
- Proven experience designing and operating multi-agent systems with orchestration patterns, state management, and production monitoring.
- Strong understanding of cloud infrastructure, preferably Google Cloud Platform, including serverless and containerized architectures.
- Experience identifying high-leverage business problems and translating them into scalable technical solutions.
- Solid grasp of LLM production challenges such as latency, cost control, fallback mechanisms, and failure handling.
- Strong communication skills with the ability to bridge technical complexity and business impact.
- Preferred: experience with vector databases, workflow orchestration tools, observability frameworks, and marketing/sales tech stacks.
- Preferred: familiarity with MCP standards, open-source ecosystems, or automation in B2B SaaS environments.
Responsibilities
- Design, build, and maintain production-grade multi-agent AI systems that automate workflows across marketing, revenue operations, and sales teams.
- Develop and scale LLM-powered infrastructure including orchestration frameworks, agent pipelines, and reusable AI components.
- Build backend services, APIs, MCP servers, and microservices that connect AI systems to enterprise tools such as CRMs, data warehouses, and communication platforms.
- Implement retrieval-augmented generation (RAG) pipelines, data integrations, and real-time context systems to enhance AI decision-making.
- Establish observability frameworks for AI systems including monitoring, evaluation, logging, performance tracking, and cost optimization.
- Define governance standards for AI workflows, including security, compliance, access control, and human-in-the-loop escalation paths.
- Partner with cross-functional teams to identify high-impact automation opportunities and translate them into scalable technical solutions.
- Build self-service automation platforms with clear documentation, APIs, and tooling to enable non-technical teams.
- Continuously improve system reliability, scalability, and efficiency through experimentation and iterative engineering.
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