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Added 4 months ago
T
Quantitative Developer - CaaS
TradingHub
Toronto, ON
- Full-time
We are seeking a Quantitative Developer to help design, build, and validate the models that power our industry-leading market surveillance and analytics products. As part of our…
Added 2 months ago
S
Quality Assurance Automation Engineer
Synechron
Calgary, AB
- $85,000 to $90,000 CAD / year
- Full-time
…Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and…
Added 2 months ago
I
Technical Engineering Seller Intern – Entry Level Sales Program 2027
IBM
Toronto, ON
- $81,600 to $115,200 CAD / year
- Full-time
At IBM, technology creates value when it solves real business challenges. As an Entry-Level Technical Engineering Seller, you will work with clients and IBM technical experts to design,…
Added 2 months ago
I
Spécialiste technique prévente – Stagiaire Programme de vente pour les talents en début de carrière 2027
IBM
Montreal, QC
- $81,600 to $115,200 CAD / year
- Full-time
At IBM, technology creates value when it solves real business challenges. As an Entry-Level Technical Engineering Seller, you will work with clients and IBM technical experts to design,…
Added 2 months ago
I
Technical Engineering Seller Intern – Entry Level Sales Program 2027
IBM
Calgary, AB
- $81,600 to $115,200 CAD / year
- Full-time
At IBM, technology creates value when it solves real business challenges. As an Entry-Level Technical Engineering Seller, you will work with clients and IBM technical experts to design,…
Added 2 months ago
I
Technical Engineering Seller Intern – Entry Level Sales Program 2027
IBM
Ottawa, ON
- $81,600 to $115,200 CAD / year
- Full-time
At IBM, technology creates value when it solves real business challenges. As an Entry-Level Technical Engineering Seller, you will work with clients and IBM technical experts to design,…
Added 2 months ago
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Manager, Customer Analytics (Sales & Marketing)
Jobber
Remote, Canada
- Full-time
- Remote
Partner with Analytics Engineering, BI, Data Science, Data Engineering, MarketingOps, and SalesOps to strengthen the data foundation, metric definitions, reporting infrastructure, and…
Added 2 months ago
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Technical Support Engineer
VGS
Canada
- Full-time
…directly into their stack, taming the complexity of payment data so they can move…
Added 2 months ago
Senior Forward Deployed Engineer (Gemini Enterprise)
Artefact
Montreal, QC
We sit at the intersection of consulting, data science, AI technologies, data engineering, and digital transformation. We do not just advise - we build, implement, and deliver results our…
Added 2 months ago
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Solutions Engineer
d1g1t
Toronto, ON
- Full-time
Programming experience in any language such as: R, Python, Julia, VBA or similar language for data analysis, scripting and prototyping a plus.
Added 2 months ago
C
Senior Design Verification Engineer
Cognichip
Toronto, ON
- $150,000 to $180,000 CAD / year
- Full-time
Our platform combines large proprietary models, agentic workflows, domain-specific engineering intelligence, and high-performance simulation infrastructure to accelerate one of the world's…
Added 2 months ago
TA
Forward Deployed Engineer (Generative AI)
Tiger Analytics
Canada
- Full-time
…firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate…
Added 2 months ago
KS
Dotnet Developer
Kumaran Systems
Toronto, ON
- Full-time
Programming Skills: You are a strong hands-on developer proficient in one or more programming languages such as C#, Python, and JavaScript/TypeScript. You have experience with modern…
Added 2 months ago
JG
DataOps/Cloud Data Engineer
J&M Group
Toronto, ON
- Full-time
Experience developing ETL processes with Informatica, Azure Data Factory and Databricks (Python, Lakeflow, and SQL Optimization)
Added 2 months ago
TI
Semantic Modeler
TS Imagine
Montreal, QC
- Full-time
This is an opportunity to work at the intersection of data engineering, semantic modeling, and applied AI - on a product that is being defined as you build it. We have multiple openings…
Added 1 month ago
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Software Engineer Production Systems - Netflix Animation Studios
Netflix
Vancouver, BC
- Full-time
Java Full Stack Software Engineers work in our Production Engineering team. We develop, maintain and improve the software systems that run all parts of our business, front-ends and back,…
Added 1 month ago
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Technical Director Lighting - Netflix Animation Studios
Netflix
Vancouver, BC
- Full-time
…TD will work closely with their craft group, the Solutions Engineering and R&D teams to enable high-quality technical outcomes and a standardised…
Added 1 month ago
Transformation Engineer - Software Development, AI & Quality Engineering
Fitch Group
Toronto, ON
Fitch Group is seeking a hands-on Transformation Engineer with strong expertise in software development, AI-enabled engineering, and modern quality practices. This role will focus on…
Added 2 months ago
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Senior Software Developer
AssistIQ
Remote, Canada
- Full-time
- Remote
As a Senior Software Developer, you will be responsible for designing, developing and managing the cloud based services that power the AssistIQ platform.
Added 2 months ago
EI
Factory Test Specialist
Ecolab Inc.
Calgary, AB
- Full-time
Founded in Calgary, Alberta in 2001, CoolIT Systems is the leading Direct Liquid Cooling technology provider for data centers, supercomputers, and desktop computers. We design and…
Added 3 months ago
SJ
Software Engineer, Applied AI
St. Joseph Communications
Toronto, ON
- $110,000 to $130,000 CAD / year
Senior engineering ability: Write clean, documented code (Python, Go, JavaScript, etc.) and use agentic coding tools (Claude Code, Codex etc) as a core part of your development workflow -…
Added 2 months ago
C
Azure Cloud and AI Lead - 69858401
Cognizant
Toronto, ON
- Full-time
Strong Python development experience with machine learning, data engineering, and IoT data processing solutions.
Added 2 months ago
BP
Data Scientist
BNP Paribas
Montreal, QC
- Full-time
Hands-on experience contributing to data science, machine learning, deep learning, or GenAI projects in Python. In particular :
Added 2 months ago
KC
Power Platform Developer (contract)
KPMG Canada
Toronto, ON
- Contract
The Power Platform Developer will design, develop, and maintain business applications, automations, and data solutions that improve operational efficiency and support data-driven…
Added 2 months ago
C
AI Engineer
Cambio
Kitchener, ON
- Full-time
Build and maintain data + inference pipelines that power AI features in production

Artefact

Senior Forward Deployed Engineer (Gemini Enterprise)

Montreal, QC, Canada

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Job details

Posting language: English
Posted: Sep 1, 2026

Original job description

About Artefact

Artefact is a next-generation data and AI consulting firm dedicated to accelerating the adoption of data and AI to create measurable business impact across the full enterprise value chain.

We sit at the intersection of consulting, data science, AI technologies, data engineering, and digital transformation. We do not just advise - we build, implement, and deliver results our clients can measure. Our teams bring together consultants, data scientists, data engineers, AI engineers, analysts, and digital experts to solve complex business challenges with pragmatic, production-ready solutions.

As Artefact continues to grow globally, we are building a team of entrepreneurial data and AI talent who can help clients move beyond experimentation and into scalable, governed, value-generating AI adoption.

The Role

Artefact is looking for a Senior Deployed AI Engineer specialized in Gemini Enterprise and the Google AI stack: an engineer who works embedded with our clients and takes AI products from idea to production.

You will design and build the interfaces, services, and agentic systems at the heart of our client work, such as conversational applications over enterprise data, multi-step agents that automate business workflows, and the retrieval and data pipelines that support them. You will own your components end to end: the front end, the service behind it, the data and retrieval pipelines feeding it, the deployment, and the evaluations proving it works.

This role combines deep, certified expertise in Google's enterprise AI stack (Gemini models, Vertex AI, and the Gemini Enterprise agent platform) with the ability to deliver end to end. Beyond your platform specialization, you will be expected to work confidently across the full delivery lifecycle - full-stack development, data engineering, cloud infrastructure, evaluation, and client communication.

You will work closely with our clients, with direct exposure from the start, and you will support the professional development of the engineers around you.

What You'll Do

Build Full-Stack AI Applications, End to End

You will build AI products across the entire stack, from interface to infrastructure.

Develop user-facing interfaces in TypeScript/React and the backend services and APIs behind them in Python or Node.
Implement agentic behavior: orchestration, tool and function calling, memory, and guardrails.
Build retrieval-augmented generation (RAG) pipelines: ingestion, chunking, embeddings, vector and hybrid search.
Connect AI systems to enterprise data and applications via APIs, semantic layers, and protocols such as MCP.

Go Deep on Gemini Enterprise and the Google AI Stack

You will be the team's reference for Google's enterprise AI platform.

Design and build agents with Gemini models, Vertex AI, the Agent Development Kit (ADK), and Agent Engine.
Implement and configure Gemini Enterprise for clients: Agent Designer for natural-language and trigger-based agents, the Inbox for managing long-running agents at scale, and agent sandboxes for autonomous problem-solving.
Connect Gemini Enterprise to the client's application landscape through first-party and partner connectors, with proper permissions, governance, and auditability.
Build grounded, retrieval-backed applications with Vertex AI Search and RAG Engine, grounding with Google Search, and BigQuery as the data backbone.
Implement agent interoperability through the A2A protocol and MCP.
Track Google's releases closely and translate new capabilities into client value quickly.

Make AI Systems Production-Grade

Our standard is production quality: systems that are reliable, monitored, and maintainable.

Write evaluation suites and regression tests for LLM-powered features, and monitor cost, latency, and quality in production.
Apply solid engineering practice: version control, code review, automated testing, CI/CD, and observability.
Deploy on cloud infrastructure (GCP, Azure, or AWS) using containers, serverless, and infrastructure-as-code.
Build and maintain the data pipelines that feed AI systems, across warehouses, lakehouses, and vector stores.

Work AI-Natively and Client-Facing

Our engineers work AI-natively and represent Artefact directly with clients.

Use agentic coding tools (Claude Code, Gemini CLI, Codex, Cursor) daily, with good judgment about verification and review.
Communicate progress, trade-offs, and blockers clearly to clients and project leads.
Support pre-sales when needed: scope solutions, build demos, and estimate effort with our partnership and consulting teams.
Mentor junior engineers and contribute to internal accelerators, reusable components, and engineering standards.

What We're Looking For

Required Experience

3–5 years of experience in software engineering or data engineering, with extensive hands-on use of AI tools and LLM-based development over the past year (professional projects, internal initiatives, or substantial personal builds).
Strong hands-on experience with the Google AI stack: Gemini models, Vertex AI, and ideally Gemini Enterprise or ADK - ideally with experience taking at least one solution to production on GCP.
Strong programming skills in Python and TypeScript/JavaScript, and experience building and consuming APIs.
Experience with front-end development (React or similar) and at least one backend framework.
Hands-on experience with RAG, embeddings, and vector search, and with at least one agentic framework (Google ADK, LangGraph/LangChain).
Strong working experience with GCP; Azure or AWS is a plus.
Fluency with agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor.
Experience building and maintaining data pipelines.
Bachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience.

Certifications

A Google Cloud certification is a strong differentiator at application. If you do not hold one yet, obtaining it within your first 2 months in the role is a requirement - Artefact sponsors the exam and gives you time to prepare.

Google Cloud Professional Machine Learning Engineer (preferred), covering Vertex AI, generative AI, and production ML.
Google Cloud Generative AI Leader is valued as a foundation, complemented by hands-on Vertex AI / Gemini Enterprise delivery experience.

Preferred Experience

Experience with MCP servers, multi-agent patterns, or LLM evaluation tooling (LangSmith, Langfuse, promptfoo).
Experience with Terraform or CI/CD pipelines.
Experience with GCP, BigQuery, or Google Workspace integrations alongside Gemini Enterprise.

Key Capabilities

A strong candidate will bring:

Deep expertise in Gemini Enterprise and the Google AI stack, combined with breadth across the full stack
Owns features end to end, from interface to infrastructure
Cares about evaluation and reliability, not just the happy path
Communicates clearly with clients in demos, documents, and code review
Client-facing mindset: understands client needs and translates business requirements into technical solutions
Learns new tools and models fast, and shares what works

Why Join Artefact

At Artefact, data and AI are not abstract strategy topics. They are tools for creating business value, improving organizations, and helping people make better decisions.

You will join a global community of data and AI experts who combine consulting, engineering, data science, marketing, and technology expertise. You will work on complex, high-impact problems with leading organizations and help shape how enterprises adopt AI responsibly and effectively.

We value action, collaboration, learning, client trust, and shared knowledge. We believe that technology only matters when it is used, adopted, and translated into impact.

Our values aren't decorations on a wall - they're how we actually work:

There is always a way - We're builders and problem-solvers. An idea only counts if it gets executed.
Client trust is won in the field - We show up, sleeves rolled up, working side by side with our clients.
If it's not used, it's useless - We build for adoption and impact, not for slide decks.
If it's not shared, we're not done - Knowledge shared compounds. We invest in each other's growth.
We learn every day - In a field that moves this fast, standing still means falling behind. We embrace the challenge.

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More about this job

Responsibilities

You will design and build end-to-end AI products, including agentic systems, RAG pipelines, and user-facing interfaces. You will work embedded with clients to deploy production-grade AI solutions while maintaining high standards for reliability and performance.

Requirements

Candidates must have 3-5 years of experience in software or data engineering with extensive hands-on use of LLMs and the Google AI stack. A bachelor's or master's degree in a relevant field is required, along with strong proficiency in Python and TypeScript.

Skills

Gemini Enterprise
Vertex AI
Python
TypeScript
React
RAG
Agentic systems
Data engineering
Cloud infrastructure
GCP
API development
Machine learning
Software engineering
Prompt engineering
CI/CD
Vector search

Benefits

Professional development support
Certification sponsorship
Global team collaboration

Visa sponsorship

Not detected in the job text

Keywords

Gemini Enterprise
Vertex AI
Google Cloud Platform
Python
TypeScript
React
RAG
Agentic AI
BigQuery
Data Engineering
Machine Learning
LLM
API
Terraform
CI/CD
Vector Databases
Software Engineering
Consulting
Artificial Intelligence
Cloud Infrastructure
Prompt Engineering
LangGraph
LangChain
MCP
Observability
Containerization
Serverless
Data Pipelines
Governance
Auditability

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