Senior MLOps Engineer (ML Workflows Engineering)

TL;DR

Senior MLOps Engineer (ML Workflows Engineering): Building and optimizing high-load backend services for distributed ML platforms, streamlining machine learning operations and enabling AI teams to focus on model development. Focus on designing robust MLOps tools, automation, and pipelines, and optimizing workflows for reproducibility, scalability, and cost-efficiency.

Location: This is a hybrid role, requiring presence in one of hirify.global' offices located in Amsterdam, Netherlands; Belgrade, Serbia; Berlin, Germany; Limassol, Cyprus; Munich, Germany; Paphos, Cyprus; Prague, Czech Republic; Warsaw, Poland; or Yerevan, Armenia. Remote work is also possible from Germany.

Company

hirify.global develops developer tools, aiming to speed up production and empower developers through AI-powered assistance and agents.

What you will do

• Build tools, automation, and workflows to simplify infrastructure for AI teams.
• Develop robust monitoring, logging, and tracing systems for ML workflow performance and reproducibility in production.
• Design, implement, and maintain end-to-end ML pipelines for seamless model development, training, and deployment.
• Work with large-scale distributed systems, including GPU clusters, to support ML model training, fine-tuning, and evaluation.
• Collaborate with product and development teams to transform high-level goals into scalable and maintainable systems.
• Optimize workflows for reproducibility, scalability, and cost-efficiency while keeping ML teams productive.

Requirements

• Hands-on experience with modern MLOps tooling, including Kubernetes, Cloud providers (GCP and AWS), and ML orchestration frameworks.
• Solid understanding of the ML lifecycle from idea to customer-facing application.
• Ability to own projects end-to-end through design, experimentation, implementation, and iteration.
• Customer-centric mindset to translate ML engineer needs into actionable architectural decisions.
• Experience with modern CI/CD systems, such as GitHub Actions or hirify.global TeamCity.
• At least three years of Python experience writing clean, maintainable code in modern ML codebases.

Nice to have

• Experience with ML orchestrators (ZenML, Dagster, Airflow), infrastructure components using Kubernetes, Python backend services, and experiment tracking/observability tools (Weights & Biases, MLflow, Langfuse).
• Familiarity with LLM inference frameworks (vLLM, DeepSpeed, TensorRT), a strong theoretical background in NLP and transformer-based approaches, and experience with Java/Kotlin.

Culture & Benefits

• Work in a company passionate about code and developing effective developer tools.
• Contribute to AI-powered assistance and agents, a core part of developer workflows in IDEs.
• Focus on removing infrastructure challenges and streamlining MLOps.
• Opportunity to build impactful ML models and intelligent agents.
• Emphasis on integrating cutting-edge MLOps practices and engineering excellence.
• Work in a hybrid format across various European locations.