About the Role
As a Forward Deployer Engineer at Rensora, you are the bridge between our engineering team and enterprise clients. You will deploy, configure, and operationalize the Evolis AI platform in diverse client environments — from air-gapped on-premises Kubernetes clusters to multi-cloud setups spanning AWS, Azure, and GCP. Your work ensures that every client deployment is stable, secure, and tailored to their specific infrastructure and compliance requirements.
This is not a traditional support role. You will be a hands-on engineer who understands the Evolis AI platform deeply enough to adapt it to challenging environments, debug complex infrastructure issues under pressure, and feed critical insights back to the product and platform teams. You will write deployment automation, build monitoring dashboards, create runbooks, and develop the tooling that makes future deployments faster and more reliable.
The ideal candidate is someone who thrives at the intersection of engineering and client engagement. You are comfortable working directly with enterprise IT teams, translating their requirements into technical solutions, and operating with a high degree of autonomy in environments where every deployment is a little different. If you enjoy the satisfaction of seeing a platform come to life in a real-world enterprise environment, this role will be deeply rewarding.
What You'll Do
- Deploy and configure the Evolis AI platform in enterprise client environments, adapting to diverse infrastructure setups including on-premises, hybrid, and multi-cloud architectures
- Build and maintain Helm charts, Terraform modules, and deployment automation scripts that enable repeatable, reliable installations across different environments
- Configure monitoring, alerting, and observability stacks (Prometheus, Grafana) tailored to each client's operational requirements and SLAs
- Collaborate directly with enterprise IT and DevOps teams to understand their infrastructure constraints, security policies, and compliance requirements
- Troubleshoot and resolve complex deployment and runtime issues across networking, storage, compute, and application layers
- Create and maintain deployment documentation, runbooks, and operational guides for both internal teams and client administrators
- Feed deployment insights and client requirements back to the platform engineering team to improve product deployability and operability
- Participate in capacity planning and architecture reviews for new client deployments, ensuring the platform is sized and configured for their workload profiles
What We're Looking For
- 3+ years of experience in deployment engineering, DevOps, site reliability, or infrastructure engineering roles
- Strong hands-on experience with Kubernetes — including deployment, networking, storage, RBAC, and troubleshooting in production environments
- Proficiency with Infrastructure as Code tools (Terraform, Helm) and configuration management
- Experience deploying software across multiple cloud providers (AWS, Azure, GCP) and on-premises environments
- Solid Linux systems administration skills and comfort with shell scripting and Python automation
- Experience building and managing monitoring and alerting systems using Prometheus, Grafana, or similar tools
- Strong communication skills and comfort working directly with enterprise clients and their technical teams
- A problem-solving mindset with the ability to debug complex issues across distributed systems under time pressure
Nice to Have
- Experience with air-gapped or highly regulated deployment environments (FedRAMP, SOC 2, HIPAA)
- Familiarity with service mesh technologies, network policies, and enterprise security tooling (Vault, cert-manager)
- Background in deploying AI/ML platforms or GPU-accelerated workloads in enterprise settings
- Experience with GitOps workflows (ArgoCD, Flux) for managing deployments declaratively
Why Rensora
- Remote-first, async culture
- Deep technical work — not ticket factories
- Competitive compensation and equity
- Continuous learning budget
- Work with cutting-edge AI systems at enterprise scale