What are the responsibilities and job description for the DevOps Engineer position at PROLIM Global Corporation?
Looking for DevOps Engineer
Location: Plano, Texas
Description
Role Summary
We are looking for a DevOps Engineer with strong experience in AI/ML platforms to design, build,
and operate scalable, secure, and reliable infrastructure that supports model development, training,
deployment, and monitoring. This role bridges DevOps and MLOps to ensure AI solutions move
smoothly from experimentation to production.
Key Responsibilities
- Design, build, and operate CI/CD pipelines for AI/ML workloads, including model training and
inference deployments
- Manage and optimize cloud infrastructure (AWS, Azure, or Google Cloud Platform) for AI platforms
- Implement MLOps practices: model versioning, reproducibility, monitoring, and rollback
- Deploy and manage containerized workloads using Docker and Kubernetes
- Automate infrastructure using Infrastructure as Code (IaC) tools (Terraform, ARM, CloudFormation,etc.)
- Monitor system performance, availability, and cost; proactively address reliability and scalability issues
- Collaborate with Data Scientists, ML Engineers, and Product teams to productionize AI models
- Ensure security, compliance, and governance for AI pipelines and data
- Support incident response, rootcause analysis, and continuous improvement
Requirements
- 3 years of experience in DevOps, SRE, or Platform Engineering
- Handson experience with cloud platforms (AWS, Azure, or Google Cloud Platform)
- Strong experience with Docker, Kubernetes, and microservices
- Experience building CI/CD pipelines (GitHub Actions, GitLab CI, Azure DevOps, Jenkins, etc.)
- Familiarity with AI/ML workflows and tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML)
- Proficiency in Python and/or scripting (Bash, PowerShell)
- Strong understanding of Linux systems, networking, and observability
Preferred Qualifications
- Experience with large-scale model deployment (LLMs, GenAI, or realtime inference systems)
- Knowledge of data pipelines (Airflow, Spark, Kafka, etc.)
- Experience with monitoring AI models (data drift, model performance, latency )
- Familiarity with security best practices for AI systems and regulated environments
- Experience in automotive, IoT, or connected systems is a plus
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