Senior MLOps Engineer, GCP

SOFTETA

. Operate and improve secure, reliable ML training and serving infrastructure on GKE and Vertex AI, optimizing GPU usage, capacity, quotas, and costs .

Posted 10/31/2026full-timeVilnius • LithuaniaSenior💰 €5,000 - €7,000 per monthWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in MLOps and platform engineering with a focus on managing production ML infrastructure, optimizing GPU usage, and implementing CI/CD pipelines. Proficient in developing workflows using Kubeflow and Vertex AI, while ensuring compliance with data governance and privacy standards.

Highest-signal resume keywords

MLOps ExperienceVertex AI ProficiencyKubernetes OperationGitOps ImplementationPython Engineering Skills

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

MLOpsProduction ML InfrastructureKubernetesKubeflow PipelinesVertex AI PipelinesCI/CDPythonModel Lifecycle ManagementData GovernanceStreaming Inference

Soft Skills

CollaborationDocumentation SkillsIndependent Ownership

Tools & Technologies

GKEBigQueryAirflowConfig SyncDataflowFlinkKafkaFeastVertex AI Feature Store

Industry Keywords

Data PrivacyGDPRDPDPService-Level ObjectivesFeature Stores

Tech Stack

Tools & technologies

AirflowBigQueryKafkaKubernetesPython

About the role

Key responsibilities & impact

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 enabling 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
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) supporting reliable platform operation and adoption

Requirements

What you’ll need

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

Benefits

Comp & perks

Diverse and technically challenging projects
Training budget for professional and personal growth
Flexible working hours
Hybrid workplace model
Flexible schedule
Agile/SCRUM environment
Technical equipment that you can choose