MLOps Engineer

Requirements

4+ years in DevOps, DataOps, or MLOps roles with hands-on experience in cloud-native environments (AWS, Azure, or GCP).
Strong background in CI/CD tooling (e.g., GitLab CI, Jenkins, ArgoCD).
Proficiency in scripting languages (e.g., Python, Bash) and configuration management.
Experience with data workflow tools such as Apache Airflow, dbt, or Kafka.
Familiarity with ML lifecycle and deployment platforms (e.g., MLflow, Kubeflow, SageMaker).
Solid understanding of data governance, monitoring, and versioning for both data and models.
Strong communication skills and experience working in cross-disciplinary teams.

Job responsibilities

Key Responsibilities
DevOps Functions
Design, implement, and maintain CI/CD pipelines for application and service deployment.
Manage container orchestration (e.g., Kubernetes, Docker) and infrastructure-as-code tools (e.g., Terraform, Ansible).
Ensure high availability, security, and scalability of systems in cloud or hybrid environments.
DataOps Integration
Build and maintain automated data ingestion and transformation pipelines.
Monitor data quality, lineage, and integrity across data workflows.
Collaborate with data engineers to ensure version control and reproducibility of data processes.
ModelOps Functions
Operationalize machine learning models in production using tools like MLflow, SageMaker, or Kubeflow.
Implement model monitoring and performance tracking (e.g., drift detection, explainability).
Coordinate with data scientists to streamline model training, validation, deployment, and rollback processes.
Cross-functional Collaboration
Work closely with developers, data scientists, and product teams to align infrastructure with business needs.
Support observability frameworks for monitoring and alerting across applications, data, and ML systems