Professional Services Deployment Engineer

ArangoDB

Deployment & Training Engineer deploying ArangoDB and ArangoAI across Kubernetes and on-prem environments. Training US customers to operate, troubleshoot, and scale contextual AI systems.

Posted 8/8/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying and managing ArangoDB and ArangoAI components across various cloud platforms and on-prem environments, with a strong focus on Kubernetes, Helm, and cloud infrastructure. Capable of training teams in cluster operations and troubleshooting complex deployment issues while ensuring effective communication and documentation.

Highest-signal resume keywords

Kubernetes DeploymentHelm ManagementAWS Cloud InfrastructureAzure Cloud InfrastructureArangoDB Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

KubernetesHelmArangoDBNeo4jAWSAzureLinuxSSHEBSMinIO

Soft Skills

Clear CommunicationPatient TrainingOwnershipTeam Confidence Building

Tools & Technologies

AKSEKSOpenShiftLoad BalancersDNSIngress Controllers

Industry Keywords

Deployment EngineeringDistributed SystemsCluster OperationsPerformance TroubleshootingDocumentation Creation

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsDNSKubernetesLinuxNeo4jOpenShift

About the role

Key responsibilities & impact

Deploy ArangoDB and ArangoAI components across AKS, EKS, OpenShift, and on-prem clusters
Build and maintain Helm-based deployment bundles, including combined stacks
Troubleshoot Kubernetes-level issues including PVC/PV mapping, storage performance, pod scheduling, cluster access, and network policies
Diagnose and resolve load balancer issues, connection timeouts, and service routing problems
Validate cluster health, storage provisioning, and performance baselines
Train customer teams to become self-sufficient in cluster operations, monitoring, and upgrades
Create documentation, training, and deployment guides
Lead hands-on workshops and labs
Provide one-time setup support while coaching teams toward long-term ownership
Investigate performance issues across compute, storage, and query layers
Review query plans, vector index usage, and full-scan patterns with DBAs and ML engineers
Help customers navigate platform constraints such as lost SSH access, misconfigured jump boxes, and degraded cluster states
Collaborate with internal engineering to surface recurring issues and improve deployment automation
Partner with platform teams to align deployment patterns
Work with product engineering to validate new features in real-world environments
Coordinate with customer project leads to ensure smooth onboarding and predictable delivery

Requirements

What you’ll need

4+ years of deployment engineering experience
Strong experience with Kubernetes, including AKS, EKS, and OpenShift
Experience with Helm
Solid understanding of AWS and Azure cloud infrastructure
Experience with on-prem environments
Familiarity with EBS, gp3, MinIO, and S3-compatible storage
Ability to debug load balancers, timeouts, service endpoints, DNS, and ingress controllers
Experience with distributed systems or databases such as ArangoDB or Neo4j
Comfort with Linux, SSH, jump boxes, and cluster-admin workflows
Clear, calm communication and ability to translate complex systems into simple explanations
Patient training approach and ability to build team confidence
Strong ownership and ability to work in fast-moving environments with shifting priorities

Benefits

Comp & perks

Contributing to cutting-edge AI and data infrastructure
Collaborating with experienced engineers, marketers, and product leaders
Helping shape how enterprises build AI-powered applications
Diverse and inclusive team
Support for employees and interns as they learn, grow, and contribute