Data Engineer / MLOps Specialist
Role Summary
Build data pipelines and model-ops infrastructure that keep AI workloads reliable, compliant, and cost-efficient.
Key Responsibilities
- Ingest, transform, and version datasets with Databricks or Snowflake.
- Create CI/CD pipelines for ML using GitHub Actions and Terraform.
- Monitor model drift, latency, and resource usage with Prometheus & Grafana.
Must-Have Qualifications
- 4+ years in data engineering or DevOps.
- Kubernetes, Docker, and GPU orchestration skills.
- Proficiency in Spark or Flink.
Preferred
- Exposure to Ray Serve, KServe, or Sagemaker.
- Certifications: Azure Data Engineer, CKAD.
Engagement: Full-time contract, 6–12 months, remote with overlap to GMT+4.
Job Types: Full-time, Permanent
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