Hybrid Machine Learning Platform – Backend Engineer

at Everseen

• Job title: Machine Learning Platform – Backend Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, Azure, BigQuery, Cloud, Distributed Systems, Docker, Google Cloud Platform, Jenkins, Kafka, Kubernetes, Python, PyTorch, Ray, Tensorflow, Terraform
• Location requirements: Hybrid, Belgrade, Serbia

About the role

• Machine Learning Platform/Backend Engineer for Everseen shaping internal ML platform and driving automation. Focus on building infrastructure and ensuring compliance with data governance.

Responsibilities

• Own the design and implementation of the internal ML platform, enabling end-to-end workflow orchestration, resource management, and automation using cloud-native technologies (GCP/Azure).
• Design and manage Kubernetes-based infrastructure for multi-tenant GPU and CPU workloads with strong isolation, quota control, and monitoring.
• Integrate and extend orchestration tools (Airflow, Kubeflow, Ray, Vertex AI, Azure ML or custom schedulers) to automate data processing, training, and deployment pipelines.
• Develop shared services for model behavior/performance tracking, data/datasets versioning, and artifact management (MLflow, DVC, or custom registries).
• Document architecture, policies, and operational runbooks to ensure platform maintainability and transparency.
• Contribute to CI/CD pipelines for ML models, integrating automated testing, deployment, and rollback mechanisms.
• Build reusable components for data ingestion, model training.
• Ensure compliance with data governance, security, and audit requirements.

Requirements

• Strong programming skills (Python)
• Hands-on experience with Kubernetes, Docker, and cloud services.
• Experience with CI/CD tools (e.g., GitLab, Jenkins).
• Understanding of ML training pipelines, data lifecycle, and model serving concepts
• Excellent communication and collaboration skills.
• Familiarity with workflow orchestration tools (e.g., Airflow, Kubeflow, Ray, Vertex AI, Azure ML).
• Understanding ML lifecycle, model versioning, and monitoring.
• Experience with ML frameworks (e.g., TensorFlow, PyTorch).
• Experience with GPU orchestration (e.g., NVIDIA GPU Operator, MIG).
• Experience with Infrastructure as Code (e.g., Terraform).
• Knowledge of data engineering tools (e.g., Snowflake, Databricks, BigQuery, Airbyte, Kafka).
• Familiarity with feature stores and model registries.
• Exposure to large-scale distributed systems and performance optimization.