Devops and mlops engineer

Role & responsibilities

Your future duties and responsibilities
We are seeking a high-caliber DevOps & MLOps Engineer to bridge the gap between robust infrastructure and cutting-edge Artificial Intelligence. In this role, you won't just be managing pipelines; you will be architecting the foundation for Generative AI and Machine Learning at scale.
You will be responsible for the end-to-end lifecycle of our applicationsfrom standard microservices to complex Large Language Models (LLMs)—ensuring they are scalable, reproducible, and secure within the Google Cloud Platform (GCP) ecosystem.

Key Responsibilities :

Infrastructure & Orchestration: Design and maintain scalable infrastructure using Terraform and manage containerized workloads via Docker and Kubernetes (GKE).
CI/CD Excellence: Build and optimize automated deployment pipelines using Cloud Build to ensure rapid, reliable delivery of both code and models.
MLOps Mastery: Implement Agent Lifecycle Management and governance frameworks. Manage Feature Stores and ensure total reproducibility of ML experiments and production runs.
GenAI Deployment: Architect specialized environments for LLM Deployment & Scaling, specifically managing high-performance GPU/TPU resource allocation and optimization.
Monitoring & Governance: Establish rigorous Model Monitoring and observability patterns using GCP's operations suite to track performance, drift, and system health.
Data Engineering Support: Collaborate on data pipelines using BigQuery, Dataflow, and Dataproc to ensure seamless data flow for ML training and inference.

Required qualifications to be successful in this role
Must to have skills :

1. Core DevOps Foundations
   Version Control: Expert-level Git (branching strategies, hooks, and security).
   IaC: Advanced Terraform for multi-environment provisioning.
   Containerization: Mastery of Docker and Kubernetes (GKE).
   Automation: Fluent in Python and Bash for custom tooling and scripting.
   Networking: Deep understanding of VPCs, Subnets, Firewalls, and Load Balancing.
2. GCP Platform Expertise
   Core Services: IAM, VPC, Cloud Storage, Logging, Monitoring.
   Compute: GKE, Compute Engine, Cloud Functions, and Cloud Run.
   Data/ML Services: Vertex AI Suite, BigQuery, Dataflow, Dataproc, and Cloud Composer (Airflow).
3. MLOps & AI Specialization
   Lifecycle: Experience with Agent orchestration and governance.
   Infrastructure for AI: Managing Feature Stores and automated Inference pipelines.
   Hardware Acceleration: Hands-on experience managing and scaling GPU/TPU clusters for heavy compute workloads.
   Generative AI: Specific experience deploying and scaling Large Language Models (LLMs).

Good to have :
GCP Certification: Professional Cloud DevOps Engineer or Professional Machine Learning Engineer certification is highly preferred.
Problem Solving: A proactive mindset regarding "Automate Everything" and the ability to troubleshoot complex distributed systems.
Communication: Ability to act as a bridge between Data Science, Software Engineering, and Security teams.

CGI is an equal opportunity employer. In addition, CGI is committed to providing accommodation for people with disabilities in accordance with provincial legislation. Please let us know if you require reasonable accommodation due to a disability during any aspect of the recruitment process and we will work with you to address your needs

Preferred candidate profile