We’re a small, high-impact, AI-Native consulting firm building at the intersection of Data and AI. We leverage cutting-edge AI to build custom, production-grade systems for our clients. We move fast, learn faster, and believe grit beats polish every time. We ship real systems, not just decks. We take pride in solving hard problems with clarity, accountability, and craft.

The Role: The Multi-Tool Engineer At Fastloop, our Technical Consultants (TC) are our engine. We are looking for a code-first generalist who can bridge the gap between a raw data lake and a functional, agentic AI application.

You aren’t just a “Data Engineer” or a “Software Engineer”; you are the “connective tissue” of modern intelligence. You will be responsible for the programmatic development of Agentic workflows, GenAI pipelines, and the cloud-native infrastructure that powers them and the integration of these systems into existing business processes to drive measurable operational impact. A TC typically owns the technical execution across 1-3 client projects simultaneously.

What You’ll Do 1. Agentic Engineering & GenAI Workflows

Build Intelligent Agents:

Design and build agent architectures and reasoning loops (ReAct, Chain of Thought, Plan-and-Solve) using Google ADK, Vertex AI Agent Builder, LangChain, or LlamaIndex.

Advanced RAG & Search : Architect Retrieval-Augmented Generation (RAG) pipelines and enterprise search indexing using Vertex AI Search and Vector Databases.

LLM Integration:

Orchestrate prompts, context management, and tool-calling for models like Gemini Enterprise.

Automated Ingestion:

Develop custom pipelines for ingesting and enriching structured and unstructured data (PDFs, text, logs)



into GCS or BigQuery.

Data Engineering for AI:

Write advanced SQL and automate dbt/Dataform pipelines to ensure data is “AI-ready.”

MLOps & Observability:

Implement CI/CD for model updates and set up AI observability/eval frameworks (eg. RAGAS, Vertex Eval) to detect and prevent hallucinations.

3. Integration & Cloud-Native Development

Inference Layer & APIs:

Design and build scalable backend services and RESTful APIs using Python (FastAPI, Flask) to expose AI capabilities (AIaaS).

Infrastructure as Code (IaC):

Architect and deploy end-to-end GenAI infrastructure (Vertex AI, Cloud Run, Cloud SQL) entirely through Terraform.

Cross-Cloud Generalist:

While we lean heavily on GCP, you maintain the “Generalist Edge” by ensuring solutions can be adapted to Azure or hybrid environments.

Process & Workflow Integration:

Build event-driven architectures that trigger AI actions based on business events (eg. Pub/Sub, Webhooks, or Cloud Scheduler). Use Cloud Workflows or custom Python logic to orchestrate multi-step business processes.

Legacy & Tool Integration:

Connect AI agents to “real world” tools – CRMs, ERPs, or custom proprietary databases – via secure API orchestration to allow agents to take action, not just provide answers.

4. Agile Execution & Quality

Daily Contribution:



Own your stories from refinement to production. Validate technical feasibility, identify constraints early, and document technical tasks/DoD.

Testing & Validation: Prepare environments and test automation for UAT. Execute technical validation and resolve defects to ensure endpoints deliver business value.

Consultant’s Mindset:

Effectively communicate technical trade-offs (e.g., speed vs. scalability) to both technical leads and non-technical stakeholders

Your Technical Toolkit

AI & GenAI Expertise: Proven experience with LLMs (Gemini preferred), prompt engineering, and context window management.

Agentic Frameworks:

Hands-on with Google ADK, LangChain, or LlamaIndex .

Modern Databases:

Expert SQL . Experience with pgvector, Pinecone, or Qdrant, alongside BigQuery and PostgreSQL.

Cloud & DevOps: Mastery of GCP (Cloud Run, GKE, Pub/Sub, IAM). Strong proficiency in Terraform and GitHub Actions. Experience with

Event-driven architecture

(Pub/Sub, Eventarc) and

Workflow Orchestration

(Google Cloud Workflows, Airflow, or Step Functions).

Backend:

Advanced Python

(FastAPI/Django). Familiarity with Document AI or unstructured data parsing is a plus.

Frontend Support: Ability to build lightweight internal UIs (Streamlit, React) for agent-facing dashboards or prototypes.

Background

3-6 years as a Cloud, AI, or Backend Engineer in a high-intensity setting.

You are a generalist who can be a SQL wizard in the morning, an AI/Python dev in the afternoon, and write IaC by end-of-day.

You don’t just write code; you own the technical outcome and the “de

📌 Technical Consultant (Vancouver)
🏢 Fastloop
📍 Vancouver