[Hiring] Senior DevOps/MLOps Engineer @Bonapolia

Role Description

We are looking for a Senior DevOps/MLOps Engineer.

Qualifications

• Tech Level: Senior
• Language Proficiency: Upper-Intermediate
• Employment type: Full time
• Candidate Location: Not Russia, Not Belarus, Not Ukraine
• Working Time Zone: CET
• Planned Work Duration: 6 months

Customer Description

A global mobility and urban services platform that allows users to book rides or other services and negotiate the fare directly with service providers. It offers a variety of services including ride-hailing, intercity travel, delivery, and task assistance, operating across multiple cities and countries and is one of the most popular mobility apps globally.

Project Phase

New phase of the project.

Soft Skills

• Highly proactive with the ability to independently identify stakeholders and drive tasks to completion
• Strong stakeholder management skills with the ability to interact effectively across different seniority levels
• Curious mindset with a focus on continuous improvement and challenging existing processes
• Excellent communication skills for effective collaboration with cross-functional teams
• Strong time management skills with a high level of organization and reliability
• Russian language is a must

Hard Skills / Must Have

• Experience with AWS architecture, security best practices, and cost optimization
• Proficiency with Databricks
• Experience with cloud-managed ML platforms such as AWS Sagemaker or Google Vertex AI
• Expert knowledge of Terraform or Terragrunt for multi-cloud infrastructure management
• Strong expertise in Kubernetes including cluster scaling and advanced networking concepts
• Hands-on experience with observability tools such as Prometheus, Grafana, Loki, or ELK
• Deep knowledge of Git-based workflows and CI/CD tools such as ArgoCD or FluxCD
• Strong understanding of Docker security and container orchestration
• Advanced skills in MLOps for continuous retraining and deployment
• Experience with ML pipeline tools such as Kubeflow or Argo Workflow
• Experience with LLMOps frameworks such as Langfuse, ollama, or vLLM

Responsibilities and Tasks

• Design and implement scalable, secure, and cost-effective MLOps solutions on cloud platforms
• Automate deployment pipelines and reduce manual effort
• Collaborate with data scientists to align solutions with MLOps architecture and best practices
• Integrate security throughout the machine learning lifecycle
• Manage issues from root cause analysis to resolution and provide feedback for prevention
• Contribute to system architecture and software design

Technology Stack

• AWS
• AWS Sagemaker
• Databricks

Interview Stages

• English check (15 minutes)
• Internal technical interview (1-1.5 hours)
• Client interview (1 hour)
• Client interview – team fit (45 minutes)

Ready to Join?

We look forward to receiving your application and welcoming you to our team!