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Added 2 mins ago
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Senior Cloud Engineer -Hybrid
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Toronto, ON · Waterloo, ON
- Full-time
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Added 2 mins ago
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- Full-time
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- Full-time
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Fastloop

Senior AI Architect (Architect-Practitioner)

Vancouver, BC, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Oct 30, 2026
Posting language: English
Posted: Jun 10, 2026

Original job description

Careers

Senior AI Architect (Architect-Practitioner)

Vancouver

Fastloop needs you to design and deliver production-grade Agentic Platforms that actually execute work, securely, reliably, and at scale. We operate at the intersection of data foundations and applied AI, helping clients move from research concepts to real, deployed systems.

We are a small, sharp consulting team. We ship real systems, not just decks. As a Principal AI Architect, you are the senior technical authority responsible for ensuring our AI solutions are not just impressive demos, but durable, governable, and operationally sound.

This is a hands-on principal IC role, not a people-management position.

You are the technical decision-maker for AI delivery at Fastloop. You bridge the gap between the art of the possible and production reality, owning decisions across model selection, RAG strategy, agent orchestration, security posture, and deployment architecture.

You lead by doing. You write the core scaffolding code, define the architectural patterns others extend, and act as the final technical gatekeeper for production AI releases. When projects hit complexity cliffs, you are called in to unblock them.

While you do not formally manage people, you are expected to teach, mentor, and elevate the technical bar across the team.

What You’ll Do

Agentic Intelligence & Reasoning
Architect agentic workflows: Design multi-step, stateful agent systems using LangGraph, CrewAI, Semantic Kernel, or equivalent frameworks.
Beyond prompting: Define how agents reason, plan, and act. Define tool selection, memory, guardrails, and failure handling.
Application Logic & Cognitive Architecture: Build the complex chains, prompt orchestration, reasoning loops, and multi-agent workflows. Maintain conversation state, manage token limits, and handle tool-calling errors gracefully in production.
Advanced RAG: Design retrieval systems end-to-end: chunking strategy, embeddings, indexing, hybrid search, grounding, and citation.
Evaluation & Metrics Ownership: Design robust evaluation frameworks (LLM-as-a-Judge, Ragas, TruLens, Arize) to measure model performance, latency, and accuracy before production deployment. Own the ‘truth’ of the system and prove that AI solutions work reliably.
Custom tooling: Build APIs and tools when off-the-shelf connectors fall short.
Technical Leadership & Delivery
Hands-on ownership: Personally implement high-impact components, core services, agent frameworks, complex integrations, and automation logic.
Rapid Prototyping for Presales: Quickly build working demos or POC’s to validate feasibility and secure client engagements.
Collaboration with Engineers: Work closely with Engineers on pipelines and infrastructure while taking ownership of cloud, agent orchestration, and deployment architecture.
Infrastructure & deployment: Define and contribute to IaC (Terraform), CI/CD pipelines, environment strategy, and release processes.
Cloud focus: Lead both GCP and Microsoft Azure technical work, filling gaps where our team has limited experience.
Security & governance: Own the production-readiness checklist for AI systems, including PII handling, IAM, VPC boundaries, secrets management, and auditability.
Engineering validation: Build rapid technical spikes and POCs to move initiatives from assumptions to proof and de-risk delivery.

You hold final technical sign-off on AI deployments.

Strategic Consulting & Practice Enablement
Pre-sales feasibility: Partner with the Director of Engineering and leadership to assess opportunities, prevent over-selling, and define realistic delivery paths.
Reusable accelerators: Convert successful project patterns into reusable modules, templates, and reference architectures that accelerate future delivery.
Client advisory: Translate technical trade-offs (latency vs. accuracy, cost vs. performance, managed vs. custom) into business-relevant decisions for senior stakeholders.
Dedicate approximately 20% of your time to mentoring team members, performing code reviews, and shaping Fastloop’s AI standards, frameworks, and internal best practices.

Your Technical Toolkit

AI & LLMs

Production experience with Vertex AI, OpenAI, Azure AI
Strong evaluation discipline using LangSmith, Arize Phoenix, or equivalent
Expert in Cognitive Architectures for multi-agent systems, using advanced patterns such as planning, reflection, and tool-use to solve complex multi-step business problems.
Deep experience with agent state management (LangGraph checkpoints, conversation history, error handling).
Production AI beyond notebooks, including RAG, A2A, MCP orchestration, and agentic workflows.
Model evaluation, latency, cost, and performance optimization.
MLOps pipelines, including automated training, CI/CD, monitoring, and deployment.

Engineering

Expert-level Python (async, FastAPI, custom tooling, service design)
Strong SQL and data modeling fundamentals
Collaborate with Data Engineers to ensure AI systems integrate seamlessly with client data pipelines, warehouses, and orchestration workflows.

Infrastructure & Ops

Hands-on experience with GCP and Microsoft Azure
CI/CD with GitHub or GitLab
Terraform and infrastructure-as-code best practices

Data & Systems

Vector databases and search (BigQuery Vector Search, Pinecone, or similar)
Solid understanding of enterprise networking, identity, and workload isolation
Solid understanding of data platforms like Synapse, Databricks and Snowflake

Frameworks & Platforms

LangChain, LangGraph, Google’s ADK
dbt / Dataform

Experience & Background

10+ years in software or data engineering with a clear evolution toward applied AI
3+ years building, deploying, and operating LLM-based systems in production
Strong consulting instincts: able to provide technical clarity under ambiguity and stakeholder pressure
Comfortable owning outcomes end-to-end, not just architecture diagrams

We’re Looking For Demonstrated Experience That Highlights

Your Consulting DNA: You thrive in multi-client environments and can provide technical clarity under high-pressure stakeholder scenarios.
The “Grit” Factor: You prefer a “sharp” team over a “big” one. You value ownership, clarity, and building the hardest parts first. ‍

‍Ready to build real AI systems? We want to hear from you.

Ready to join the team?

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More about this job

Responsibilities

Design, build, and oversee production-grade agentic AI systems, including agent orchestration, RAG, evaluation, cloud deployment, infrastructure, and security. Provide technical leadership through hands-on implementation, production sign-off, client advisory, reusable accelerators, and mentoring.

Requirements

Requires 10+ years of software or data engineering experience, including 3+ years building and operating production LLM systems, plus expert Python skills and experience with agent architectures, RAG, and evaluation. Candidates should have hands-on GCP and Azure experience, infrastructure-as-code and CI/CD knowledge, strong consulting skills, and the ability to own delivery end to end.

Skills

Agentic AI Architecture
Multi-Agent Systems
Python
Retrieval-Augmented Generation
LLM Evaluation
LangGraph
Cloud Architecture
Google Cloud Platform
Microsoft Azure
Terraform
CI/CD
AI Security and Governance
Vector Search
MLOps
SQL
Technical Consulting

Visa sponsorship

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Keywords

Agentic AI
LangGraph
CrewAI
Semantic Kernel
Google ADK
Python
FastAPI
SQL
Retrieval-Augmented Generation
Vertex AI
OpenAI
Azure AI
LangSmith
Arize Phoenix
Ragas
TruLens
Terraform
GitHub
GitLab
CI/CD
MLOps
BigQuery Vector Search
Pinecone
Databricks
Snowflake
Synapse
dbt
Dataform
Model Context Protocol
A2A Orchestration

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