Senior MLOps Engineer (GCP)

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SeniorZdalnie·KrakówFull-time

#471117·Dodano 5 dni temu·1

Źródło: Softeta

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Tech Stack / Keywords

GCPVertex AIGKE Kubernetes BigQueryKubeflow PipelinesAirflowGitOpsPython CI/CD

Podsumowanie oferty

Own and improve a telecommunications client’s GCP-based ML platform, enabling data scientists to train and deploy models independently. The work focuses on reliable ML infrastructure using GKE, Vertex AI, and related platform technologies.

Wymagania

5+ years of experience in MLOps or platform engineering, including at least 2 years of end-to-end ownership of production ML infrastructure.
Strong production experience with Vertex AI, GKE, and BigQuery, alongside in-depth Kubernetes cluster operation and troubleshooting skills.
Experience with Kubeflow Pipelines or Vertex AI Pipelines, alongside Airflow.
Practical experience with GitOps, Config Sync or equivalent tools, and CI/CD for model deployment.
Strong Python engineering skills, including unit testing and packaging.
Ability to independently own a company-wide platform and build self-service tools supported by clear documentation and measurable SLOs.

Nice to have:

Experience with streaming, online inference, feature stores, and model observability using tools such as Dataflow, Flink, Kafka, Feast, or Vertex AI Feature Store.
Experience serving and evaluating LLMs, alongside GPU scheduling and cost optimization on GKE.
Knowledge of data governance and privacy requirements, including DataHub, policy tags, GDPR, and DPDP; experience scaling consumer-facing products.

Obowiązki

Operate and improve secure, reliable ML training and serving infrastructure on GKE and Vertex AI, optimizing GPU usage, capacity, quotas, and costs.
Build reusable templates, CI/CD pipelines, and self-service tools that enable data scientists to deploy models independently.
Manage the model lifecycle, including registry, versioning, deployment, rollback, drift monitoring, and serving health.
Develop workflows using Kubeflow or Vertex AI Pipelines and collaborate with data engineers on feature and training data flows across BigQuery and Airflow.
Manage infrastructure as code through GitOps, using Config Sync or equivalent tools.
Enforce least-privilege access to personally identifiable information and data protected by policy tags. Create practical documentation and define service-level objectives (SLOs) to support reliable platform operation and adoption.

Benefity

Training budget for professional and personal growth.
Flexible working hours and schedule.
Hybrid workplace model.
Technical equipment that you can choose.

Dofinansowanie szkoleń

Elastyczne godziny

Softeta

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