DevOps

Complexio; Warsaw (IN); On-site; USD 100,000 - 130,000; Full time

Benefits offered by this job

Opportunity for professional growth

Collaborative team environment

Continuous learning in a dynamic field

Job summary

Complexio in Warsaw, Indiana, is searching for a skilled DevOps engineer to define and create robust platforms for deploying and managing distributed systems across various environments. The role offers the opportunity to work with cutting-edge technologies in AI and cloud solutions, ensuring seamless delivery of innovative products.

Candidates should have a strong background in cloud infrastructure management, Kubernetes proficiency, and programming experience. This role combines technical and problem-solving skills within a collaborative team environment.

Qualifications

• 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.
• Proficient in managing and scaling Kubernetes clusters.
• Strong understanding of network infrastructure and security principles.

Responsibilities

• Define and create the platform for deploying and managing distributed systems.
• Ensure infrastructure is robust, scalable, and secure.
• Combine cloud technologies with database management expertise.
• Document and explain technical details clearly and effectively.

Skills

Cloud infrastructure management

Kubernetes proficiency

Programming (Python, Java, Go)

CI/CD pipelines

Containerization (Docker)

Network infrastructure and security

Problem-solving skills

Education

M.Sc or Ph.D in Computer Science or related field

Tools

AWS

Azure

Google Cloud

Neo4j

Job description

About the Role

As a DevOps engineer at our AI product company, you will define and create the platform for deploying, managing, and optimizing our distributed systems across on-premises, multiple cloud environments (AWS, Azure, Google Cloud), and Kubernetes.

Our system leverages multiple LLMs, Graph and Vector Databases and integrates data from multiple sources to power our AI solutions. You will ensure our infrastructure is robust, scalable, and secure, supporting the seamless delivery of our innovative products. This role requires combining cloud technologies and database management expertise, embracing the challenges of integrating AI and machine learning workflows on modern GPUs.

Qualifications

• 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 management, specifically with Neo4j and vector databases, including setup, scaling, and optimization for performance and reliability
• Experience deploying and running Machine Learning Solutions, including LLMs

Benefits

• 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.