Softeta is a software engineering partner for finance, energy, industrial, and other high-stakes sectors.

We specialize in building and modernizing backend-heavy, integration-critical systems where speed, accuracy, and reliability are non-negotiable. With more than 100 AI and custom software experts across engineering hubs in Lithuania and Poland, we embed ourselves directly in client operations, delivering custom software, automation, and AI where they create the most value. Clients such as SEB, NordVPN, and carVertical trust us with systems that serve millions of users worldwide.

We’re looking for a Senior MLOps Engineer to own and improve a telecommunications client’s GCP-based ML platform, enabling data scientists to train and deploy models independently.

What You will do

• 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.

Requirements

• 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.

Benefits

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