Senior DevOpsMLOps 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 (1-15hour)

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!