Lead Forward Deployed Engineer

Responsibilities

• Lead end-to-end deployments of Dynamo AI products into customer-controlled Kubernetes environments.
• Configure and troubleshoot Helm releases, workloads, services, ingress, storage, networking, identity, secrets, observability, and scaling.
• Work with customer infrastructure, platform, security, networking, application, and operations teams.
• Translate security, reliability, compliance, and operational requirements into deployment architectures.
• Diagnose production and pre-production issues across Kubernetes, cloud infrastructure, application services, databases, authentication, networking, and service dependencies.
• Build and maintain Helm configurations, Terraform modules, deployment scripts, validation tools, runbooks, and operational documentation.
• Own technical discovery, deployment planning, effort estimation, dependency tracking, risk assessment, acceptance criteria, and production-readiness reviews.
• Maintain customer deployment plans and communicate progress, decisions, risks, blockers, and resolution paths.
• Drive technical issues to closure across customer and internal product and engineering teams.
• Identify recurring product and deployment gaps and help convert customer-specific solutions into reusable platform improvements.
• Establish repeatable deployment, validation, upgrade, and operational-handoff practices.

Requirements

• 5+ years of relevant professional experience in infrastructure engineering, platform engineering, site reliability engineering, production engineering, DevOps, cloud engineering, or deeply hands-on customer engineering.
• Demonstrated experience deploying and operating production applications on Kubernetes.
• Experience configuring and troubleshooting Kubernetes workloads, services, ingress, DNS, network policies, persistent storage, secrets, identity, autoscaling, and observability.
• Production experience with at least one major cloud platform: AWS, Azure, or Google Cloud.
• Hands-on experience with Helm and at least one infrastructure or deployment automation system such as Terraform, Argo CD, Flux, or Kustomize.
• Strong debugging skills across containers, distributed services, networking, logs, metrics, authentication, storage, and service dependencies.
• Experience designing or operating systems meeting production requirements for reliability, scalability, security, monitoring, upgrades, and incident response.
• Ability to independently own complex customer deployments and resolve ambiguous technical problems.
• Ability to manage deployment plans, dependencies, risks, customer actions, milestones, and operational readiness.
• Strong written and verbal communication skills, including explaining technical decisions and tradeoffs to engineering and non-engineering stakeholders.
• Direct experience diagnosing and resolving Kubernetes and infrastructure-level deployment issues.

Nice-to-haves

• Experience deploying enterprise software into customer-owned cloud, private-cloud, hybrid, or on-premises environments.
• Experience with regulated or security-sensitive organizations.
• Experience with ingress controllers, service meshes, API gateways, TLS, private endpoints, proxies, DNS, firewall rules, and enterprise network restrictions.
• Experience with PostgreSQL, MongoDB, Redis, object storage, and managed-cloud equivalents.
• Experience with monitoring and observability platforms using logs, metrics, traces, dashboards, and alerts.
• Experience supporting production incidents, upgrades, migrations, capacity planning, high availability, backup, or disaster recovery.
• Previous AI or machine-learning experience is helpful but not required.

Compensation and Benefits

• Work directly with organizations deploying AI in critical real-world operations.
• Gain exposure to enterprise architectures, governance models, and operational workflows across regulated industries.
• Help shape deployment methodologies and product evolution for responsible enterprise AI adoption.