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myPOS Logo myPOS · IT - Software Development

Ml Platform Engineer

Romania, Bucharest B2B · Job Posted June 15, 2026
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Job Description

As an ML Platform / MLOps Engineer, you will design, build, and operate the infrastructure, tooling, and pipelines that make machine learning reliable at scale. You'll sit at the intersection of data engineering, DevOps, and applied ML - owning the platforms and systems that let data scientists and engineers move from experiment to production safely and repeatably. Your work will power intelligent products and internal automation across the company, and will help shape how the organisation adopts ML and AI responsibly.

Job Responsibility

  • Build and maintain MLOps automation end-to-end: CI/CD for models and pipelines, environment management, artifact versioning (models, data, prompts, code), and release governance
  • Implement and operate model serving infrastructure: deployment patterns (blue/green, canary, shadow), endpoint management, scaling, and latency/throughput optimisation
  • Build and maintain training and experimentation infrastructure: job orchestration, compute provisioning, experiment tracking, hyperparameter management, and reproducibility tooling
  • Implement observability for ML systems: data quality checks, feature drift detection, model performance monitoring, bias checks, alerting, and incident response workflows
  • Build and maintain data pipelines for ingestion, transformation, feature engineering, and export across multiple sources and destinations
  • Design and maintain a feature store or feature platform layer: serving consistency, point-in-time correctness, and reuse across teams
  • Expose well-governed datasets, features, and APIs that models, pipelines, and downstream consumers can rely on
  • Enforce secure data handling and compliance with relevant data protection standards, access controls, and audit requirements
  • Contribute to documentation, platform standards, and continuous improvement of ML engineering processes across teams

Requirements

  • Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience)
  • 5+ years of Data or ML Engineering experience, with at least 3 years shipping ML systems to production
  • Strong Python skills (typed code, async, testing) and solid SQL fluency
  • Hands-on MLOps experience: model registries, experiment tracking (MLflow or Vertex Experiments), pipeline orchestration, and reproducible training runs
  • Strong DevOps fundamentals: CI/CD (GitHub Actions, Cloud Build, or similar), IaC (Terraform), containerization (Docker)
  • Familiarity with at least one major cloud provider (GCP, AWS, Azure) and deploying data solutions in the cloud
  • Experience building and maintaining data pipelines with orchestrators (Airflow/Composer, Dagster) and distributed engines (Spark, BigQuery)
  • Strong troubleshooting mindset: ability to debug issues across data, infra, pipelines, and deployments
  • Collaborative mindset and clear communication across engineering, analytics, and business stakeholders

Nice to have

  • Strong GCP experience and ecosystem knowledge: Vertex AI (Model Garden, Pipelines, Endpoints, Experiments, Monitoring), BigQuery, Composer, Dataproc, Cloud Run, Dataplex, Cloud Storage
  • Experience with data governance concepts: access control, retention, data classification, auditability, and compliance standards
  • Model monitoring experience: drift detection, data quality issues, performance degradation, bias checks, and alerting strategies
  • Experience building and maintaining agentic applications or LLM-powered tools using frameworks such as LangGraph, LlamaIndex, or the Anthropic/OpenAI Agents SDKs
  • Familiarity with MCP (Model Context Protocol) or comparable tool/function-calling protocols for LLM integrations

What we offer

  • Vibrant international team operating in hi-tech environment
  • Annual salary reviews, promotions and performance bonuses
  • myPOS Academy for upskilling and training
  • Unlimited access to courses on LinkedIn Learning
  • Annual individual training and development budget
  • Refer a friend bonus as we know that working with friends is fun
  • Teambuilding, social activities and networks on a multi-national level
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