# Job Description: MLOps & AI Platform Engineer **Job Title:** MLOps & AI Platform Engineer **Experience:** 3–11 Years **Location:** Islamabad (On-site/Hybrid as per business requirement) **Employment Type:** Full-Time ## Job Overview We are seeking a skilled **MLOps & AI Platform Engineer** with **3–11 years of experience** to build, automate, and manage scalable machine learning platforms and production AI environments. The ideal candidate will have hands-on expertise in MLOps, Kubernetes, cloud-native AI infrastructure, CI/CD automation, and model lifecycle management. You will be responsible for enabling data scientists and AI engineers to efficiently develop, deploy, monitor, and maintain machine learning models at scale. ## Key Responsibilities - Design, build, and maintain enterprise-grade MLOps platforms and AI infrastructure. - Develop and automate end-to-end machine learning pipelines for training, validation, deployment, and monitoring. - Implement model versioning, experiment tracking, and model registry solutions. - Build scalable CI/CD pipelines for AI/ML workloads. - Deploy and manage machine learning workloads on Kubernetes-based environments. - Collaborate with Data Scientists, AI Engineers, Data Engineers, and DevOps teams to operationalize ML solutions. - Implement Infrastructure as Code (IaC) for cloud-native AI platforms. - Monitor platform health, model performance, and infrastructure availability. - Ensure platform security, scalability, reliability, and operational excellence. - Troubleshoot production issues and continuously optimize platform performance. ## Required Technical Skills ### MLOps Platforms - Hands-on experience with **Kubeflow or Vertex AI Pipelines or SageMaker Pipelines**. - Strong experience with **MLflow** for experiment tracking, model registry, and lifecycle management. - Experience orchestrating machine learning workflows using **Apache Airflow**. ### Containerization & Orchestration - Strong expertise in **Kubernetes (GKE or AKS or EKS)**. - Experience deploying and managing containerized AI/ML workloads in cloud environments. ### Infrastructure Automation - Hands-on experience with **Terraform** for Infrastructure as Code (IaC). - Experience automating infrastructure provisioning and cloud resource management. ### CI/CD & DevOps - Experience with **GitHub Actions** for CI/CD automation. - Knowledge of DevOps best practices, Git workflows, and automated deployments. ### Monitoring & Observability - Experience using **Prometheus** for infrastructure and application monitoring. - Knowledge of logging, alerting, and performance monitoring for AI platforms. ## Qualifications - Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Information Technology, or a related field. - **3–11 years** of professional experience in MLOps, DevOps, Platform Engineering, Cloud Engineering, or AI Infrastructure. - Strong scripting and automation skills using Python, Bash, or similar languages. - Excellent analytical and problem-solving skills. - Experience working in Agile/Scrum environments. ## Preferred Skills - Experience with Docker and containerized application deployment. - Knowledge of cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform. - Familiarity with model monitoring, drift detection, and automated retraining pipelines. - Experience implementing security best practices for AI/ML platforms. - Cloud and Kubernetes certifications are a plus. ## Key Technology Stack - **MLOps Platforms:** **Kubeflow or Vertex AI Pipelines or SageMaker Pipelines** - **Workflow Orchestration:** **Apache Airflow** **and** MLflow - **Container Orchestration:** **Kubernetes (GKE or AKS or EKS)** - **Infrastructure as Code:** Terraform - **CI/CD:** GitHub Actions - **Monitoring:** Prometheus - **Cloud Platforms:** **Google Cloud Platform or Microsoft Azure or Amazon Web Services** (Preferred) - **Automation:** Python **and** Bash (Preferred)