Senior DevOps/MLOps Engineer

We are looking for a Senior DevOps/MLOps Engineer:

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 (1-1,5hour)

• 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!