ITDS

Lead MLOps Engineer — Cloud & AI Infrastructure

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SeniorHybryda·WarszawaFull-time

#461883·Dodano 25 dni temu·2

Źródło: ITDS

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Tech Stack / Keywords

MLOpsPython Go Terraform Kubernetes AWS SageMaker GitHub Actions Prometheus Spark

Podsumowanie oferty

Build scalable MLOps infrastructure for deploying and maintaining machine learning models based on proprietary data. Design low-latency model serving and automated training pipelines for a client in the semiconductor and consumer electronics sector.

Wymagania

Minimum 3 years of experience developing distributed microservices and cloud infrastructure.
Strong proficiency in Python and familiarity with Go.
Hands-on experience with Infrastructure as Code and orchestration tools such as Terraform and Kubernetes.
Solid knowledge of AWS services such as SageMaker, Lambda, EMR, and EC2.
Practical experience with CI/CD pipelines using GitHub Actions or ArgoCD.
Familiarity with monitoring and alerting systems such as Prometheus and Grafana.
Experience with big data tools and frameworks such as Spark, Hadoop, and Airflow.
Understanding of mainstream ML frameworks such as TensorFlow and PyTorch.

Nice to have:

Certifications in cloud or DevOps technologies.
Experience with advanced ML deployment architectures or data engineering.

Obowiązki

Design and develop scalable machine learning infrastructure for low-latency environments.
Serve machine learning models to downstream applications securely and reliably.
Manage model versioning and implement rollback mechanisms.
Develop and implement monitoring tools to detect performance issues and data drift.
Build and maintain automated data preprocessing and model training pipelines.
Integrate ML serving systems with data pipelines, monitoring, and alerting frameworks.
Optimize serving systems for efficiency, speed, and reliability.
Research and prototype innovative ML architectures and serving technologies.
Streamline deployment workflows with robust testing practices.
Collaborate with ML engineers to operationalize production-grade models at scale.

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