MLOps & AI Platform Engineer
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Key skills for this role
About the Role
Datamatics seeks an MLOps & AI Platform Engineer with 3-11 years of experience to build, automate, and manage scalable machine learning platforms and production AI environments. The role requires hands-on expertise in MLOps, Kubernetes, cloud-native AI infrastructure, CI/CD automation, and model lifecycle management.
Key Skills for This Role
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.
Requirements
- 3–11 years of professional experience in MLOps, DevOps, Platform Engineering, Cloud Engineering, or AI Infrastructure.
- 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.
- Strong expertise in Kubernetes (GKE or AKS or EKS).
- Hands on experience with Terraform for Infrastructure as Code (IaC).
- Experience with GitHub Actions for CI/CD automation.
- Experience using Prometheus for infrastructure and application monitoring.
- Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Information Technology, or a related field.
- Strong scripting and automation skills using Python, Bash, or similar languages.
Full Job Posting
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.
Required Technical Skills Containerization & Orchestration
- Strong expertise in Kubernetes (GKE or AKS or EKS).
- Experience deploying and managing containerized AI/ML workloads in cloud environments.
Required Technical Skills Infrastructure Automation
- Hands on experience with Terraform for Infrastructure as Code (IaC).
- Experience automating infrastructure provisioning and cloud resource management.
Required Technical Skills CI/CD & DevOps
- Experience with GitHub Actions for CI/CD automation.
- Knowledge of DevOps best practices, Git workflows, and automated deployments.
Required Technical Skills 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)
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