Artificial Intelligence Engineer

Smart IT Frame LLC; Halifax; On-site; CAD 120,000 - 170,000; Full time

Job summary

Smart IT Frame LLC is seeking an experienced AI Platform Engineer to design, build, and maintain scalable AI platforms supporting ML workloads and GenAI applications. You will develop cloud-native infrastructure across AWS/Azure/GCP and scale AI workloads using Kubernetes and Docker.

Collaboration with data scientists and DevOps teams will drive production-ready AI solutions. The role emphasizes MLOps/LLMOps practices, CI/CD pipelines, and infrastructure automation with Terraform/Ansible, while

Qualifications

• Experience in platform engineering, DevOps, cloud engineering, or ML infrastructure.
• Hands-on experience with AWS, Azure, or GCP.
• Proficiency with Kubernetes, Docker, and containerized deployment.
• Experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps).
• Infrastructure as Code with Terraform, CloudFormation, or equivalent.
• Knowledge of Python and scripting for automation.
• Experience with MLOps/LLMOps tools and AI model deployment workflows.
• Understanding of LLMs, Generative AI, RAG architectures, embeddings, and vector databases.
• Experience with monitoring and observability tools (Prometheus, Grafana, ELK).
• Strong networking, IAM, security, and production reliability knowledge.

Responsibilities

• Design, build, and maintain scalable AI platforms supporting ML, Generative AI, and LLM-based apps.
• Develop and manage cloud-native infrastructure using AWS, Azure, or GCP.
• Deploy, manage, and scale AI workloads with Kubernetes, Docker, and orchestration tech.
• Build and maintain CI/CD pipelines for AI models, releases, and infra automation.
• Implement MLOps/LLMOps for lifecycle management, versioning, deployment, monitoring, and improvement.
• Support deployment/integration of LLMs, RAG pipelines, vector databases, and AI agent frameworks.
• Automate infrastructure provisioning and configuration with Terraform, Ansible, or similar.
• Establish monitoring, logging, observability, security, and governance standards.
• Optimize platform performance, scalability, reliability, and cloud cost.
• Collaborate with data scientists, ML engineers, AI developers, and DevOps teams for production-ready solutions.
• Implement access controls, secrets management, data protection, and responsible AI practices.

Skills

Platform engineering

DevOps culture

MLOps/LLMOps

Cloud architecture

Security & IAM

Networking

Tools

AWS

Azure

GCP

Kubernetes

Docker

Terraform

Ansible

CI/CD

Python scripting

Prometheus

Grafana

ELK

Job description

We are looking for an experienced AI Platform Engineer to design, build, and maintain scalable infrastructure and platforms that support AI/ML workloads, Generative AI applications, and agentic AI solutions. The ideal candidate will have strong experience in cloud infrastructure, Kubernetes, MLOps/LLMOps, automation, and deploying AI models and services into production environments.

Key Responsibilities

• Design, build, and maintain scalable AI platforms supporting machine learning, Generative AI, and LLM-based applications.
• Develop and manage cloud-native infrastructure using AWS, Azure, or GCP.
• Deploy, manage, and scale AI workloads using Docker, Kubernetes, and container orchestration technologies.
• Build and maintain CI/CD pipelines for AI model deployment, application releases, and infrastructure automation.
• Implement MLOps and LLMOps practices for model lifecycle management, versioning, deployment, monitoring, and continuous improvement.
• Support the deployment and integration of LLMs, RAG pipelines, vector databases, and AI agent frameworks.
• Automate infrastructure provisioning and configuration management using Terraform, Ansible, or similar tools.
• Establish monitoring, logging, observability, security, and governance standards for AI platforms.
• Optimize platform performance, scalability, reliability, and cloud infrastructure costs.
• Collaborate with data scientists, ML engineers, AI developers, and DevOps teams to deliver production-ready AI solutions.
• Implement access controls, secrets management, data protection, and responsible AI platform practices.

Required Skills

• Strong experience in platform engineering, DevOps, cloud engineering, or ML infrastructure.
• Hands-on experience with AWS, Azure, or GCP.
• Proficiency in Kubernetes, Docker, and containerized application deployment.
• Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps.
• Infrastructure as Code experience using Terraform, CloudFormation, or equivalent tools.
• Knowledge of Python and scripting for automation.
• Experience with MLOps/LLMOps tools and AI model deployment workflows.
• Understanding of LLMs, Generative AI, RAG architectures, embeddings, and vector databases.
• Experience with monitoring and observability tools such as Prometheus, Grafana, ELK, or cloud-native monitoring services.
• Strong understanding of networking, IAM, security, and production system reliability.