Data/MLOps Engineer
⚲ Gliwice
Do uzgodnienia
Wymagania
- Python
- PySpark
- Apache Spark
Opis stanowiska
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Data/MLOps Engineer (CT&C Engineering)
For our Client, we are looking for a Data/MLOps Engineer to join their CT&C Engineering team. In this role, you will bridge the gap between data science and production, ensuring that scalable data solutions provide efficient ingestion, transformation, storage, and real-time analysis.
If you have a strong background in ML, solid PySpark skills, and know AWS SageMaker inside out, this role is for you!
Quick Job Details
• Rate: 140 – 150 PLN/h net
• Form of Cooperation: B2B Contract
• Start Date: ASAP
• English: Minimum B2 level
Who Our Client Is Looking For
We need a technical expert who brings overall ML background knowledge and can specifically address these core needs:
• The Bridge to Production: You can confidently face off with Data Scientists (who often produce notebooks only) and successfully implement their work into production-quality models.
• ML Model Expertise: You understand different ML models, know how to monitor them, and clearly understand their pros and cons.
• Hands-on Implementation: You are technically capable of building and executing these solutions using PySpark and AWS SageMaker.
Technical Stack
• Languages & Frameworks: Python, PySpark, PyTorch, SQL
• Data Processing: Apache Spark, ETL/ELT
• Cloud & Infrastructure: AWS CDK, AWS Lambdas, AWS SageMaker, Terraform / CloudFormation
• Methodology & Tools: Agile, CI/CD, Training Design
Key Responsibilities
1. ML & Data Infrastructure
• Deploy and maintain end-to-end ML lifecycles (automated training, deployment, and versioning).
• Build and support core MLOps components like Feature Stores, experiment tracking, and model registries.
• Manage scalable cloud infrastructure using Infrastructure as Code (IaC) and develop robust CI/CD/CT (Continuous Training) pipelines.
2. Data Engineering & Pipeline Optimization
• Build high-volume ingestion and processing pipelines using Apache Spark and PySpark.
• Implement data models and storage optimizations for low-latency inference and high-performance analytics.
• Integrate automated data quality checks and observability.
3. Governance, Security & Collaboration
• Proactively monitor model drift, data quality, and system latency.
• Maintain strict versioning for data, code, and artifacts to guarantee 100% reproducibility.
• Operate within an Agile framework, collaborate with Data Scientists and Product Owners, and provide clear technical documentation.
Data/MLOps Engineer (CT&C Engineering)
For our Client, we are looking for a Data/MLOps Engineer to join their CT&C Engineering team. In this role, you will bridge the gap between data science and production, ensuring that scalable data solutions provide efficient ingestion, transformation, storage, and real-time analysis.
If you have a strong background in ML, solid PySpark skills, and know AWS SageMaker inside out, this role is for you!
Quick Job Details
• Rate: 140 – 150 PLN/h net
• Form of Cooperation: B2B Contract
• Start Date: ASAP
• English: Minimum B2 level
Who Our Client Is Looking For
We need a technical expert who brings overall ML background knowledge and can specifically address these core needs:
• The Bridge to Production: You can confidently face off with Data Scientists (who often produce notebooks only) and successfully implement their work into production-quality models.
• ML Model Expertise: You understand different ML models, know how to monitor them, and clearly understand their pros and cons.
• Hands-on Implementation: You are technically capable of building and executing these solutions using PySpark and AWS SageMaker.
Technical Stack
• Languages & Frameworks: Python, PySpark, PyTorch, SQL
• Data Processing: Apache Spark, ETL/ELT
• Cloud & Infrastructure: AWS CDK, AWS Lambdas, AWS SageMaker, Terraform / CloudFormation
• Methodology & Tools: Agile, CI/CD, Training Design
Key Responsibilities
1. ML & Data Infrastructure
• Deploy and maintain end-to-end ML lifecycles (automated training, deployment, and versioning).
• Build and support core MLOps components like Feature Stores, experiment tracking, and model registries.
• Manage scalable cloud infrastructure using Infrastructure as Code (IaC) and develop robust CI/CD/CT (Continuous Training) pipelines.
2. Data Engineering & Pipeline Optimization
• Build high-volume ingestion and processing pipelines using Apache Spark and PySpark.
• Implement data models and storage optimizations for low-latency inference and high-performance analytics.
• Integrate automated data quality checks and observability.
3. Governance, Security & Collaboration
• Proactively monitor model drift, data quality, and system latency.
• Maintain strict versioning for data, code, and artifacts to guarantee 100% reproducibility.
• Operate within an Agile framework, collaborate with Data Scientists and Product Owners, and provide clear technical documentation.
🔍 Dekoder Ogłoszenia
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bridge the gap between data science and production
Oznacza to, że będziesz odpowiedzialny za przekształcanie prototypowych modeli z notatników Data Scientistów w gotowe do produkcji, skalowalne rozwiązania.
🔴
confidently face off with Data Scientists (who often produce notebooks only)
Sugeruje to potencjalną potrzebę radzenia sobie z niedojrzałymi lub niekompletnymi dostarczeniami od Data Scientistów i samodzielnego dopracowywania ich do standardów produkcyjnych.
🟡
ensure that scalable data solutions provide efficient ingestion, transformation, storage, and real-time analysis
To szeroki zakres obowiązków, który może obejmować wszystko od konfiguracji podstawowych potoków danych po budowanie złożonych systemów analizy w czasie rzeczywistym.
🟡
know AWS SageMaker inside out
Oczekuje się dogłębnej, praktycznej wiedzy i doświadczenia z AWS SageMaker, a nie tylko ogólnej znajomości.
🟡
Training Design
Może oznaczać zarówno tworzenie materiałów szkoleniowych dla innych, jak i projektowanie procesów uczenia modeli, co wymaga doprecyzowania.