DevOps/MLOps Engineering Consultant - Office of the CTO at Sonatus

Kraków, Lesser Poland Voivodeship, Poland

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Automotive, AIIndustries

Requirements

8+ years of experience building and scaling production-grade cloud services and systems, with a strong focus on DevOps, MLOps, and/or SRE
"Systems thinker" with demonstrated ability to architect end-to-end solutions and deep understanding of the full CI/CD pipeline and machine learning lifecycle
Deep proficiency in Python and Infrastructure as Code (e.g., Terraform, Pulumi)
Experience with MLOps tools (e.g., MLflow, Kubeflow, Vertex AI) and production monitoring frameworks
Ability to enforce reproducibility, approvals, audit trails, PII handling, model cards, and policy/compliance (e.g., privacy, evals, guardrails)
Experience with robust ML deployment systems (e.g., Kubeflow, MLflow, model servers like BentoML or TensorFlow Serving)
Hands-on experience with public cloud platforms (GCP, AWS, and/or Azure) and containerization/orchestration (Docker, Kubernetes)
Experience packaging, versioning, and deploying software modules and AI models (batch & online) with blue/green or canary rollouts; building feature & model registries, and automating retraining
Experience with PyTorch, vLLMs, and GPUs (plus)
Experience with tracking Modes and Agentic drift (plus)
Experience with tuning serving stacks (GPU/CPU utilization, batching, quantization) (plus)

Responsibilities

Design and build the foundational, end-to-end DevOps and MLOps platform for Generative AI systems, making critical decisions that span large language model-based systems evaluation, monitoring, and deployment
Implement the full DevOps and MLOps framework, building CI/CD/CT (Continuous Integration/Delivery/Training) automation that takes models from experiment to production with velocity and reliability
Deploy, scale, and optimize model serving infrastructure, managing GPU/NPU resources, minimizing inference latency, and building robust monitoring to ensure AI is fast, accurate, and cost-effective
Create a single, cohesive set of best practices for the entire AI lifecycle, defining model versioning, infrastructure as code, and production observability in one seamless system

Skills

DevOps

MLOps

CI/CD

SRE

Infrastructure as Code

Generative AI

Large Language Models

Model Training

Model Serving

Observability

Cloud Infrastructure

Sonatus

Platform for software-defined vehicle development

About Sonatus

Sonatus provides a platform for developing software-defined vehicles, focusing on a no-code solution that allows automotive companies to create flexible software architectures. This platform enables the collection and analysis of real-time diagnostic data from vehicles, which helps manufacturers like Hyundai Motor Group to continuously enhance vehicle quality and the ownership experience. Unlike competitors, Sonatus offers a comprehensive solution that spans the entire vehicle lifecycle, from design to after-sales services, allowing for faster innovation and cost reduction. The goal of Sonatus is to drive continuous improvement in automotive software, capitalizing on the increasing demand for software-defined vehicles globally.

Sunnyvale, CaliforniaHeadquarters

2018Year Founded

$107MTotal Funding

SERIES_ACompany Stage

Data & Analytics, Automotive & TransportationIndustries

51-200Employees