Senior MLOps Engineer (GCP)

Softeta

4d•Hybrid

About The Position

Softeta is seeking a Senior MLOps Engineer to own and improve a telecommunications client’s GCP-based machine learning platform, enabling data scientists to train and deploy models independently. The role involves operating and improving secure, reliable ML training and serving infrastructure, building reusable platform capabilities, and managing model lifecycle and data workflows. The engineer will also support platform adoption through practical documentation and measurable service-level objectives.

Requirements

At least 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, plus in-depth Kubernetes cluster operation and troubleshooting skills.
Experience with Kubeflow Pipelines or Vertex AI Pipelines, as well as 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 Haves

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.

Responsibilities

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 so data scientists can deploy models independently.
Manage model registration, 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.