MLOps & AI Platform Engineer
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Key skills for this role
About the Role
Datamatics Technologies is 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.
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
- 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
- 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
- Experience with GitHub Actions for CI/CD automation
- Experience using Prometheus for infrastructure and application monitoring
- Strong scripting and automation skills using Python, Bash, or similar languages
- Excellent analytical and problem solving skills
- Experience working in Agile/Scrum environments
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
- 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)
- Experience deploying and managing containerized AI/ML workloads in cloud environments
- Hands on experience with Terraform for Infrastructure as Code (IaC)
- Experience automating infrastructure provisioning and cloud resource management
- Experience with GitHub Actions for CI/CD automation
- Knowledge of DevOps best practices, Git workflows, and automated deployments
- 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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