Full-Stack AI Developer

CoFoMo Inc.; Montreal (administrative region); Hybrid; CAD 95,000 - 140,000; Full time

Job summary

CoFoMo Inc. is seeking a senior Data & AI Platform Engineer to advance production-grade AI solutions and build scalable data architectures in cloud environments.

You will own MLOps pipelines, deploy models, and collaborate with data scientists and software engineers to industrialize prototypes into robust, production-ready services.

Qualifications

• Hold an undergraduate degree in software engineering, computer science, or a related field.

Responsibilities

• Contribute to the deployment and industrialization of AI solutions by putting AI/ML models and solutions into production in reliable, reproducible, secure and scalable environments.

Skills

MLOps

DevOps

Cloud computing

APIs design

Data engineering

Automation

AI/ML deployment

Python

Linux

Docker

Kubernetes

Terraform

Ansible

AWS

Azure

GCP

Education

Bachelor's degree in software engineering, computer science, or related field

Tools

Terraform

Ansible

AWS

Azure

GCP

Python

Linux

Docker

Kubernetes

CI/CD pipelines

Job description

Here is a brief overview of the tasks and responsibilities:

• Contribute to the deployment and industrialization of AI solutions by putting AI/ML models and solutions into production in reliable, reproducible, secure and scalable environments;
• Develop and maintain MLOps pipelines required for the AI solution lifecycle, including integration, testing, deployment, versioning, and monitoring;
• Design, deploy, and maintain cloud infrastructures using Infrastructure as Code (IaC) approaches to ensure reproducibility and automation of environments;
• Deploy and operate AI applications, services, and components in containerized environments;
• Design, implement, and optimize ETL/ELT pipelines for structured and unstructured data using modern data engineering tools and frameworks;
• Integrate data from multiple sources, including internal systems, APIs, and external datasets, into centralized data platforms such as data lakes and data warehouses;
• Develop software components, APIs, scripts and services to integrate Data/AI solutions with customers' existing systems;
• Develop and maintain logical and physical data models that meet analytics, AI/ML, and reporting needs;
• Optimize the performance, scalability, and reliability of data systems, including query optimization and resource management;
• Implement data validation, cleansing, and monitoring processes to ensure accuracy, consistency, and compliance with governance frameworks;
• Implement continuous integration and deployment (CI/CD) mechanisms for data pipelines, software components, and AI solutions;
• Contribute to the monitoring, diagnosis and continuous improvement of solutions deployed in production;
• Contribute to ensuring the security and confidentiality of the data and solutions deployed as well as compliance with regulatory requirements;
• Maintain clear documentation of data pipelines, architectures, deployments, and processes;
• Collaborate closely with data scientists and AI engineers to transform prototypes and proofs of concept into robust, production-ready solutions;
• Adapt to the needs of different projects and quickly explore, evaluate and appropriate new technologies when necessary;
• Stay on top of trends in data engineering, MLOps, cloud technologies, and AI industrialization practices;
• Actively contribute to the continuous improvement of internal processes.

The profile we are looking for is as follows:

• Hold an undergraduate degree in software engineering, computer science, or a related field;
• Accumulate three (3) to five (5) years of relevant professional experience in data engineering, software development, MLOps, DevOps, or AI/ML deployment, ideally in several different projects, technologies, or contexts;
• Demonstrate versatility in different dimensions of solution engineering, including software development, data, cloud, infrastructure, automation, or AI/ML;
• Have deployed and operationalized solutions in production environments;
• Master the principles of Infrastructure as Code (IaC), especially with Terraform, Ansible or equivalent tools;
• Understand MLOps practices and tools as well as the full lifecycle of an ML solution, from development to deployment and monitoring;
• Master CI/CD practices and tools as well as the automation of deployment processes;
• Strong software development skills, including designing APIs, services, scripts, and integrations needed to operationalize solutions;
• Understand the principles related to computer networking and security in cloud environments;
• Have a good knowledge of machine learning and ML concepts in order to collaborate effectively with data scientists and contribute to the industrialization of their models;
• Quickly deepen your knowledge and appropriate new technologies according to the needs of the projects;
• Master Python at an advanced level;
• Move with ease in the Microsoft environment;
• Experience with cloud platforms, including AWS or Azure;
• knowledge of GCP is an asset;
• Proficiency in Linux, Docker, and containerized environments;
• experience with Kubernetes or cloud orchestration services is a strong asset.