DevOps & Data Engineer

Title: DevOps & Data Engineer - ML Infrastructure & Analytics

Salary:

Working Days:

Working Hours:

Contract Duration:

Location:

Description of Position:

• Design and implement the multi-tenant cloud infrastructure on AWS/GCP
• Build and maintain the data pipeline from integrations to Snowflake data warehouse
• Deploy and scale SLM instances across GPU-enabled infrastructure
• Implement MLOps pipelines for model training, deployment, and monitoring
• Ensure security, compliance, and data isolation across tenants
• Set up monitoring, alerting, and performance optimization

Desired Experience:

• Deployed and scaled a multi-tenant B2B SaaS platform
• Built data warehouse and ETL pipelines for analytics
• Managed ML model deployment and inference serving
• Implemented complete CI/CD with security scanning
• Production Kubernetes and container orchestration

Required TechStack:

• Infrastructure:

  Terraform, CloudFormation, Ansible

• Cloud:

  AWS (EC2, EKS, S3, RDS), GCP

• Data:

  Snowflake, dbt, Airflow, Spark, Kafka

• MLOps:

  Kubernetes, Docker, MLflow, Kubeflow, Prometheus

• Monitoring:

  Grafana, Datadog, CloudWatch, ELK Stack

• Security:

  VPC, IAM, encryption, SOC 2 compliance

Commitement

• Expectation:

  Must demonstrate strong ownership, clear communication, and the ability to collaborate effectively within a remote technical team