Montréal [Hybrid] - DevOps - MLOps engineer

QUANTEAM - North America (RAINBOW PARTNERS Group); Montreal (administrative region); On-site; CAD 95,000 - 150,000; Full time

Job summary

QUANTEAM - North America (RAINBOW PARTNERS Group) is seeking an experienced DevOps - MLOps engineer to design, build and maintain robust AI infrastructures and CI/CD pipelines in production. You will work with AI developers, data scientists and client infra teams in an Agile setup to industrialize AI deployments.

You will use vibe coding and AI tools to accelerate scripting, IaC configurations and automation, while enforcing security and cost optimization across cloud environments (AWS, Azure,

Qualifications

• 5+ years of experience in DevOps/SRE.
• Experience with vibe coding using AI tools to generate scripts and diagnose incidents.
• Expertise in MLOps and deploying AI models in production.
• Cloud platforms (AWS, Azure, GCP) and AI services.
• Infrastructure as Code (Terraform, CloudFormation, ARM Templates).
• Strong skills in containerization and orchestration (Docker, Kubernetes, Helm).
• Knowledge of MLOps tools (MLflow, Kubeflow, Weights & Biases, SageMaker Pipelines).
• Proficiency in scripting (Bash, Python) and automation.
• Experience in monitoring and observability (Prometheus, Grafana, ELK, DataDog).
• Knowledge of DevSecOps security practices and secrets management (Vault, AWS Secrets Manager).
• Ability to use AI to diagnose and resolve incidents; pragmatic and rigorous.
• Autonomy, collaboration and quick incident response.

Responsibilities

• Design and implement Cloud architectures for AI solutions (scalable, secure, optimized).
• Deploy and manage Infrastructure as Code (Terraform, CloudFormation).
• Implement CI/CD pipelines for applications and AI models.
• Make expert use of vibe coding to generate scripts, configurations and automations.
• Automate deployments and rollbacks (blue/green, canary).
• Configure monitoring, alerting and observability for AI models in production.
• Manage environments (dev, staging, production) and access control.
• Optimize Cloud costs and performance (GPU, compute, storage).
• Support developers on tooling and DevOps best practices.
• Produce technical documentation of infrastructures and procedures.
• Implement DevSecOps security practices and security compliance.
• Share MLOps and vibe coding best practices with the team.

Skills

DevOps/SRE

Vibe coding with AI tools

MLOps

Cloud platforms

Infrastructure as Code

Docker / Kubernetes

CI/CD pipelines

Monitoring/ observability

DevSecOps security

Secrets management

Scripting Bash Python

Agile collaboration

Tools

Terraform

CloudFormation

ARM Templates

Kubernetes

Docker

Helm

Prometheus

Grafana

ELK

DataDog

Vault

AWS Secrets Manager

SageMaker Pipelines

Job description

As the founding entity of RAINBOW PARTNERS, Quanteam is a consulting firm specializing in Banking, Finance, and Financial Services. Guided by our core values of closeness, teamwork, diversity, and excellence, our team of 1,000 expert consultants, representing 35 different nationalities, collaborates across 10 international offices: Paris, Lyon, New York, Montreal, London, Brussels, Geneva, Lisbon, Porto and Casablanca.

We are looking for a DevOps - MLOps engineer for one of our clients.

Missions

ABOUT THE JOB

DevOps Engineer with expertise in MLOps and mastery of vibe coding, able to build and maintain robust infrastructures for AI while automating deployments and ensuring the performance of production solutions.

CONTEXT AND ROLE OBJECTIVES

As part of IT Innovation projects, you will join the AI Factory/Innovation team as an AI DevOps Engineer. You will be responsible for designing, implementing and maintaining the infrastructures, CI/CD pipelines and environments required to deploy and operate AI solutions in production.

You will work in agile mode, closely with AI developers, Data Scientists, Solution Architects and the client's infrastructure teams. You will play a key role in the industrialization of AI solutions and the automation of deployment processes.

As an expert in vibe coding, you use generative AI tools (GitHub Copilot, Cursor, Claude, ChatGPT) to accelerate the creation of scripts, Infrastructure-as-Code configurations and CI/CD pipelines, and to quickly resolve production incidents.

RESPONSIBILITIES

• Design and implement Cloud architectures for AI solutions (scalable, secure, optimized).
• Deploy and manage Infrastructure as Code (Terraform, CloudFormation).
• Implement CI/CD pipelines for applications and AI models.
• Make expert use of vibe coding to generate scripts, configurations and automations.
• Automate deployments and rollbacks (blue/green, canary).
• Configure monitoring, alerting and observability for AI models in production.
• Manage environments (dev, staging, production) and access control.
• Optimize Cloud costs and performance (GPU, compute, storage).
• Support developers on tooling and DevOps best practices.
• Produce technical documentation of infrastructures and procedures.
• Implement DevSecOps practices and security compliance.
• Share MLOps and vibe coding best practices with the team.

Profile

REQUIREMENTS

Mandatory

Required Technical Skills:

• At least 5 years of experience in DevOps/SRE.
• Demonstrated experience in vibe coding using AI tools to generate scripts, configurations and diagnose incidents.
• Expertise in MLOps and deployment of AI models in production.
• Proficiency with Cloud platforms (AWS, Azure, GCP) and Cloud AI services.
• Expertise in Infrastructure as Code (Terraform, CloudFormation, ARM Templates).
• Strong skills in containerization and orchestration (Docker, Kubernetes, Helm).
• Knowledge of MLOps tools (MLflow, Kubeflow, Weights & Biases, SageMaker Pipelines).
• Proficiency in scripting (Bash, Python) and automation.
• Experience in monitoring and observability (Prometheus, Grafana, ELK, DataDog).
• Knowledge of DevSecOps security practices.
• Experience with secrets and configuration management (Vault, AWS Secrets Manager).
• Ability to use AI to quickly diagnose and resolve incidents.
• Pragmatism: balance between automation and delivery timelines.
• Rigour in designing and securing infrastructures.
• Innovative mindset and continuous technology watch.
• Autonomy and proactivity in identifying issues.
• Strong service orientation and support for development teams.
• Collaborative and pedagogical mindset.
• High responsiveness when dealing with production incidents.

Nice to Have

• Experience with GPUs and optimization of AI resources.