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Added 27 mins ago
Machine Learning Engineer Intern
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Canada
Internship
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Fastloop

Technical Consultant (Data & AI)

Vancouver, BC, Canada

Job type: Full-time
Apply by: Oct 3, 2026
Posting language: English
Posted: Jun 10, 2026

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Original job description

Careers

Technical Consultant (Data & AI)

Full Time

Vancouver

At Fastloop, we don’t sugarcoat it.

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

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.
Data Foundation & AI Pipelines
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.
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.
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 environment.
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 “definition of done.”
You understand that an AI model is useless if it doesn’t fit into the client’s actual workday. You take pride in ensuring the “plumbing” between the AI and the business process is seamless and robust.
Demonstrated ability to switch between different client environments and tech stacks multiple times a day without losing focus.

Preferred Experience & “Nice To Haves”

While the core of our stack is GCP-native AI, the ideal Fastloop consultant is a platform-agnostic problem solver. We value the following “extra gears”:

Multi-Cloud & Hybrid Expertise: Hands-on experience with Azure (Azure OpenAI Service, AI Search) or AWS (Bedrock, Sagemaker) to support clients in multi-cloud environments.
AI Observability & Reliability: Experience with evaluation frameworks (eg. RAGAS, Vertex Eval) and observability tooling to detect and prevent LLM hallucinations or drift.
Advanced Agentic Reasoning: Deep understanding of advanced reasoning techniques such as Chain of Thought (CoT), ReAct, or Plan-and-Solve architectures.
Data Science Lite: Experience with Google Document AI or advanced unstructured data parsing libraries for complex RAG preparation.
Frontend Fluency: Experience with React, Angular, Streamlit or Gradio for building polished internal prototypes or agent-facing interfaces.
Industry Context: Previous experience in Energy sectors or Heavy Industry helping you understand the business logic behind the data.
Professional Certifications: Google Cloud Professional ML Engineer, Cloud Developer, or Azure Solutions Architect certifications.

The Grit Factor

At Fastloop, we move fast. We are looking for a consultant who understands that:

Ambiguity is an Opportunity: You don’t wait for a perfectly groomed Jira ticket. You fill in the blanks and keep moving.
Troubleshooting is tenacious: When a pipeline fails or an API returns a 500, you dive into the logs and find the root cause rather than waiting for instructions.
Outcome is more important than Ego: You value shipping a working system in the real world over achieving “theoretical perfection” on a whiteboard.
We are built to build: You are energized by hard problems, real constraints, and production accountability.

Ready to join? Apply today.

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Responsibilities

Build and integrate production-grade AI agents, GenAI and RAG pipelines, data foundations, APIs, and cloud infrastructure for client projects. Own technical delivery from planning through testing and production, connecting AI systems to business workflows and existing tools.

Requirements

The role seeks a generalist with 3–6 years of cloud, AI, or backend engineering experience, advanced Python and SQL skills, and hands-on experience with LLMs, agent frameworks, and GCP. Candidates should be able to build and deploy cloud-native systems with Terraform, integrate AI into business processes, and communicate technical trade-offs to varied stakeholders.

Skills

Python
SQL
Generative AI
Agentic AI
Retrieval-Augmented Generation
Google Cloud Platform
Terraform
FastAPI
LangChain
LlamaIndex
Vertex AI
BigQuery
PostgreSQL
CI/CD
Event-Driven Architecture
REST APIs

Visa sponsorship

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Keywords

Google Cloud Platform
Vertex AI
Google ADK
LangChain
LlamaIndex
Gemini
Retrieval-Augmented Generation
Vector Databases
BigQuery
PostgreSQL
pgvector
Pinecone
Qdrant
Python
FastAPI
Flask
Terraform
GitHub Actions
Cloud Run
Google Kubernetes Engine
Pub/Sub
Google Cloud Workflows
Dataform
dbt
RAGAS
Vertex Eval
REST APIs
Event-Driven Architecture
Document AI
Azure OpenAI Service

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