DevOps Engineer

Complexio

DevOps engineer defining and managing platforms for distributed systems integration at an AI product company. Ensuring robust, scalable, and secure cloud infrastructure across various environments.

Posted 7/3/2026full-timeWarsaw • 🇵🇱 PolandSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying and managing cloud infrastructure across AWS, Azure, and Google Cloud, with a strong focus on Kubernetes environments and cloud-native technologies. Proficient in programming and scripting for automation, with a solid understanding of network security principles and data management in distributed systems.

Highest-signal resume keywords

Cloud Infrastructure ManagementKubernetes Cluster ManagementCI/CD Pipeline ImplementationProgramming in Python, Java, or GoDatabase Management with Neo4j

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Cloud InfrastructureKubernetesCI/CD PipelinesContainerization (Docker)Programming (Python, Java, Go)Scripting for AutomationNetwork Security PrinciplesData IntegrationMachine Learning SolutionsDatabase Management

Soft Skills

Problem-SolvingTechnical CommunicationCollaborationStructured Working Approach

Tools & Technologies

AWSAzureGoogle CloudKubernetesDockerNeo4jVector Databases

Certifications & Qualifications

M.Sc or Ph.D in Computer Science

Industry Keywords

Distributed SystemsCloud-Native TechnologiesData Protection RegulationsAI IntegrationMachine Learning Workflows

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsDockerGoJavaKubernetesNeo4jPython

About the role

Key responsibilities & impact

Define and create the platform for deploying, managing, and optimizing distributed systems across on-premises, multiple cloud environments (AWS, Azure, Google Cloud), and Kubernetes.
Ensure our infrastructure is robust, scalable, and secure, supporting the seamless delivery of our innovative products.
Combine cloud technologies and database management expertise, embracing the challenges of integrating AI and machine learning workflows on modern GPUs.

Requirements

What you’ll need

Preferred M.Sc or Ph.d degree in Computer Science or a related field
At least 7 years of experience deploying and managing cloud infrastructure (AWS, Azure,
Google Cloud)
At least 3 years experience in working with kubernetes environments
Proficient in managing and scaling Kubernetes clusters, including monitoring, troubleshooting, and ensuring high availability
Experience with cloud-native technologies, CI/CD pipelines, and containerization tools (e.g., Docker)
Familiarity with data integration and management from multiple sources in a distributed system environment
Proficiency in at least one programming language (Python, Java, Go), and experience with scripting for automation
Strong understanding of network infrastructure and security principles, ensuring compliance with data protection regulations
Excellent problem-solving and technical skills
Experience in building platforms with custom deployment models
Structured working approach and ability to piecemeal on long-term goals.
Ability to document and explain technical details clearly, with solid communication and collaboration skills
Interest and experience in working on early-stage software and solving various problems
Nice to have:
Proficient in database managment, speciffically with Neo4j and vector databases, including setup, scaling, and optimization for performance and reliability
Experience deploying and running Machine Learning Solutions, including LLMs

Benefits

Comp & perks

Join a pioneering joint venture at the intersection of AI and industry transformation.
Work with a diverse and collaborative team of experts from various disciplines.
Opportunity for professional growth and continuous learning in a dynamic field.