Summary
✨ AI‑Generated
Join a hybrid platform engineering team in Halifax, working four days per week from the office. You will develop CI/CD services and DevOps capabilities using modern programming and automation tools, coach application teams, champion developer experience, evaluate new technologies, and evolve internal engineering platforms.
Highlights
AI platform and DevOps engineering role with a strong focus on CI/CD, developer experience, automation, and platform evolution. Offers opportunities to build value-added services, coach application teams, evaluate new tools, run proofs of concept, and improve engineering practices.
Description
Role: AI Platform Engineer with DevOps Experience - DevOps, Docker (Software), GitHub, GitHub Actions, Information Technology (IT) Infrastructure, Kubernetes, Programming Languages, Red Hat Ansible,Software Product Design, Software Product Technical Knowledge, Software GenAI, LLM
Location: Halifax, NS- Hybrid (4 Days WFO)
What will you do?
· Develop value-add services which integrate with the existing DevOps stack
· Develop CICD pipelines using GitHub Actions, TypeScript, Python, Groovy and Jenkins
· Coach application teams on how to leverage DevOps offerings
· Champion developer-experience and best practices in conjunction with DevOps to ensure no developer is left behind
· Participate in new tool adoption, POC process and provide recommendations
· Evolve DevOps services beyond current state for application teams
What do you need to succeed?
Must-have:
· An engineer mindset, SDLC experience with production class delivery, strong analytical mindset, communication skills, and sense of ownership / drive
· 2+ years of development experience in one of the following languages: Java, Python, Javascript/TypeScript, C#, C/C++, Python
· Experience with application and system design patterns
· Experience with Docker or Kubernetes
· Experience with Agile methodologies, ie SCRUM
• Design, build, and operate enterprise AI/GenAI platforms supporting Large Language Models (LLMs) and AI-powered applications.
• Develop scalable LLMOps and MLOps frameworks for model deployment, monitoring, versioning, evaluation, and governance.
• Build reusable AI services including:
· Prompt Management Frameworks
· Retrieval Augmented Generation (RAG) architectures
· Embedding and Vector Search Services
· AI Gateway and Inference APIs
· Agentic AI Orchestration Frameworks
Nice-to-have:
· Experience with Elastic Search and Kibana
· Experience using DevOps CICD tools such as GitHub, GitHub Actions, Jenkins, UrbanCodeDeploy
· Experience with a public cloud technology, i.e.
Azure, AWS
· Experience building or supporting distributed applications
