👉Senior ML Engineer (AI/ML Platform)
⚲ Wrocław
Do uzgodnienia
Wymagania
- GCP
- VertexAI
- Python
- TensorFlow
- PyTorch
- MLflow
Opis stanowiska
🟣 You will be:
• developing scalable ML solutions by applying software engineering best practices and transforming exploratory code into modular, reusable, and testable Python packages,
• implementing experiment tracking, reproducibility, and versioning practices to ensure traceability of ML workflows and results,
• designing and building distributed training pipelines with robust checkpointing, fault tolerance, and standardized model evaluation frameworks,
• creating production-ready model packaging, serving, and deployment solutions, including versioned containers, canary releases, rollback procedures, and online/batch inference,
• building and maintaining ML monitoring and retraining pipelines covering data drift detection, prediction quality monitoring, and continuous model evaluation,
• defining and enforcing ML lifecycle governance, including observability, documentation, operational runbooks, and model retirement processes,
• collaborating with cross-functional teams while taking ownership of ML engineering deliverables and promoting security-first engineering practices.
🟣 Your profile:
• proven experience as an ML Engineer in production environments,
• strong proficiency in Python and modern ML frameworks (TensorFlow, PyTorch),
• hands-on experience with: ML lifecycle tooling (MLflow, Vertex AI, or equivalent), distributed training and scalable compute environments, containerization (Docker) and deployment pipelines,
• experience with cloud-native ML platforms, preferably Google Cloud / Vertex AI,
• solid understanding of: model evaluation beyond accuracy (fairness, robustness, monitoring) and CI/CD for ML systems (MLOps practices),
• familiarity with artifact management and version control systems.
🟣 Nice to have:
• experience building enterprise AI/ML platforms supporting multiple teams/products,
• knowledge of data governance, lineage, and compliance frameworks Exposure to high-scale ML systems and real-time inference architectures,
• experience implementing automated retraining and adaptive learning systems.
🟣 Recruitment Process:
CV review – HR call – Interview – Client Interview – Decision
🎁 Benefits 🎁
✍ Development:
• development budgets of up to 6,800 PLN,
• we fund certifications e.g.: AWS, Azure,
• access to Udemy, O'Reilly (formerly Safari Books Online) and more,
• events and technology conferences,
• technology Guilds,
• internal training,
• Xebia Upskill.
🩺 We take care of your health:
• private medical healthcare,
• multiSport card - we subsidise a MultiSport card,
• mental Health Support.
🤸♂️ We are flexible:
• B2B or employment contract,
• contract for an indefinite period.
• developing scalable ML solutions by applying software engineering best practices and transforming exploratory code into modular, reusable, and testable Python packages,
• implementing experiment tracking, reproducibility, and versioning practices to ensure traceability of ML workflows and results,
• designing and building distributed training pipelines with robust checkpointing, fault tolerance, and standardized model evaluation frameworks,
• creating production-ready model packaging, serving, and deployment solutions, including versioned containers, canary releases, rollback procedures, and online/batch inference,
• building and maintaining ML monitoring and retraining pipelines covering data drift detection, prediction quality monitoring, and continuous model evaluation,
• defining and enforcing ML lifecycle governance, including observability, documentation, operational runbooks, and model retirement processes,
• collaborating with cross-functional teams while taking ownership of ML engineering deliverables and promoting security-first engineering practices.
🟣 Your profile:
• proven experience as an ML Engineer in production environments,
• strong proficiency in Python and modern ML frameworks (TensorFlow, PyTorch),
• hands-on experience with: ML lifecycle tooling (MLflow, Vertex AI, or equivalent), distributed training and scalable compute environments, containerization (Docker) and deployment pipelines,
• experience with cloud-native ML platforms, preferably Google Cloud / Vertex AI,
• solid understanding of: model evaluation beyond accuracy (fairness, robustness, monitoring) and CI/CD for ML systems (MLOps practices),
• familiarity with artifact management and version control systems.
🟣 Nice to have:
• experience building enterprise AI/ML platforms supporting multiple teams/products,
• knowledge of data governance, lineage, and compliance frameworks Exposure to high-scale ML systems and real-time inference architectures,
• experience implementing automated retraining and adaptive learning systems.
🟣 Recruitment Process:
CV review – HR call – Interview – Client Interview – Decision
🎁 Benefits 🎁
✍ Development:
• development budgets of up to 6,800 PLN,
• we fund certifications e.g.: AWS, Azure,
• access to Udemy, O'Reilly (formerly Safari Books Online) and more,
• events and technology conferences,
• technology Guilds,
• internal training,
• Xebia Upskill.
🩺 We take care of your health:
• private medical healthcare,
• multiSport card - we subsidise a MultiSport card,
• mental Health Support.
🤸♂️ We are flexible:
• B2B or employment contract,
• contract for an indefinite period.
🔍 Dekoder Ogłoszenia
🔴
developing scalable ML solutions by applying software engineering best practices and transforming exploratory code into modular, reusable, and testable Python packages
Oczekuje się, że będziesz przekształcać prototypowy kod w dobrze zaprojektowane, produkcyjne rozwiązania, co może wymagać znaczącego refaktoringu.
🔴
implementing experiment tracking, reproducibility, and versioning practices to ensure traceability of ML workflows and results
Może to oznaczać, że obecne procesy są chaotyczne i będziesz musiał od podstaw wprowadzić standardy śledzenia eksperymentów.
🔴
defining and enforcing ML lifecycle governance, including observability, documentation, operational runbooks, and model retirement processes
Oczekuje się, że będziesz tworzyć i wdrażać formalne procesy zarządzania cyklem życia modeli, co może być czasochłonne i wymagać dużej dyscypliny.
🟡
collaborating with cross-functional teams while taking ownership of ML engineering deliverables
Będziesz musiał efektywnie komunikować się z różnymi zespołami i brać pełną odpowiedzialność za swoje zadania, co może oznaczać dodatkowe obowiązki.
🟢
proven experience as an ML Engineer in production environments
Szukają kogoś, kto już rozwiązywał realne problemy produkcyjne, a nie tylko pracował nad projektami akademickimi.