OVA.Work
Posted 76 days agoVerified live 2d ago
MLOps Engineer
Brief overview
Remote
UndergradOr in progress
3+ yrsMinimum
PythonLinuxShell ScriptingGitDockerKubernetesCI/CD pipelinesGitHub ActionsGitLab CI/CDJenkinsMachine Learning workflowsModel deploymentAWSMicrosoft AzureGoogle CloudREST APIsMicroservices architecture
About the company
OVA.Workova.work
OVA is the most advanced Automated, Intelligent, intuitive On-boarding platform for Staffing Firms of all sizes.
Job description
Summary
OVA.Work is seeking a skilled MLOps Engineer to design, build, and maintain scalable machine learning infrastructure and deployment pipelines. The ideal candidate will bridge the gap between data science and software engineering by automating the end-to-end machine learning lifecycle.
Responsibilities
- Design, develop, and maintain scalable MLOps pipelines for machine learning model development and deployment
- Automate model training, testing, validation, deployment, monitoring, and retraining workflows
- Build and manage CI/CD pipelines for machine learning applications
- Deploy and manage machine learning models on cloud platforms and Kubernetes environments
- Implement model versioning, experiment tracking, and artifact management
- Monitor model performance, data drift, concept drift, latency, and system health
- Develop feature stores, model registries, and automated retraining pipelines
- Collaborate with data scientists and ML engineers to optimize models for production environments
- Ensure security, scalability, reliability, and compliance of ML infrastructure
- Optimize infrastructure costs and resource utilization
- Maintain technical documentation, deployment procedures, and operational best practices
Skills
- Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Software Engineering, or a related field
- 3–6 years of experience in MLOps, DevOps, Machine Learning Engineering, or Cloud Engineering
- Strong proficiency in Python
- Experience with Linux, shell scripting, and Git
- Hands-on experience with Docker and Kubernetes
- Experience building CI/CD pipelines using GitHub Actions, GitLab CI/CD, Jenkins, or Azure DevOps
- Knowledge of machine learning workflows and model deployment practices
- Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud
- Strong understanding of REST APIs and microservices architecture
- Experience with MLOps platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, Azure Machine Learning, or Databricks
- Familiarity with workflow orchestration tools such as Apache Airflow, Prefect, or Argo Workflows
- Experience with feature stores, model registries, and experiment tracking
- Knowledge of Infrastructure as Code (IaC) tools such as Terraform or CloudFormation
- Experience with monitoring and observability tools such as Prometheus, Grafana, ELK Stack, or OpenTelemetry
- Understanding of LLMOps, Generative AI deployment, Retrieval-Augmented Generation (RAG), and vector databases
Qualifications
Must Haves
- Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Software Engineering, or a related field
- 3–6 years of experience in MLOps, DevOps, Machine Learning Engineering, or Cloud Engineering
- Strong proficiency in Python
- Experience with Linux, shell scripting, and Git
- Hands-on experience with Docker and Kubernetes
- Experience building CI/CD pipelines using GitHub Actions, GitLab CI/CD, Jenkins, or Azure DevOps
- Knowledge of machine learning workflows and model deployment practices
- Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud
- Strong understanding of REST APIs and microservices architecture
Nice to Haves
- Experience with MLOps platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, Azure Machine Learning, or Databricks
- Familiarity with workflow orchestration tools such as Apache Airflow, Prefect, or Argo Workflows
- Experience with feature stores, model registries, and experiment tracking
- Knowledge of Infrastructure as Code (IaC) tools such as Terraform or CloudFormation
- Experience with monitoring and observability tools such as Prometheus, Grafana, ELK Stack, or OpenTelemetry
- Understanding of LLMOps, Generative AI deployment, Retrieval-Augmented Generation (RAG), and vector databases
Benefits
- Competitive salary and performance-based incentives.
- Comprehensive health and wellness benefits.
- Flexible or hybrid work arrangements.
- Learning, certification, and conference sponsorship opportunities.
- Access to modern cloud infrastructure, GPUs, and AI platforms.
- Opportunity to work on cutting-edge AI, machine learning, and cloud technologies in a collaborative environment.
