Lead Forward Deployed Engineer (UK)

About Dynamo AI

At Dynamo AI, we help enterprises deploy AI systems that are reliable, secure, observable, scalable and production-ready.

As organizations accelerate adoption of Generative AI, they face growing challenges around reliability, governance, latency, safety, compliance, and operational control. Dynamo AI provides the infrastructure, evaluation frameworks, and real-time guardrails that allow enterprises to confidently operationalize AI at scale.

We work closely with enterprises and regulated industries to bridge the gap between AI experimentation and production deployment - enabling organizations to deploy AI systems safely, efficiently, and responsibly.

About the Role

We are hiring a Lead Forward Deployed Engineer to own the technical delivery of Dynamo AI’s products within enterprise customer environments running Kubernetes.

This is a deeply hands-on individual-contributor role. It is not a people-management, project-management, pre-sales, or primarily advisory position.

You will deploy, configure, troubleshoot, and operationalize DynamoEval, DynamoGuard, and AgentWarden in customer-controlled environments, including EKS, AKS, GKE, OpenShift, and on-premises Kubernetes platforms.

You will typically serve as the directly responsible technical owner for two to four strategic enterprise customers. You will lead each deployment from architecture and technical discovery through implementation, production readiness, rollout, and operational handoff.

The role combines three areas of responsibility:

• Hands-on Kubernetes and cloud infrastructure engineering
• End-to-end ownership of enterprise customer deployments
• Technical project leadership and customer communication

Previous AI or machine-learning experience is helpful but not required. Strong production infrastructure and Kubernetes deployment experience is substantially more important.

What You Will Do

• Lead end-to-end deployments of Dynamo AI products into customer-controlled Kubernetes environments.
• Personally configure and troubleshoot Helm releases, workloads, services, ingress, storage, networking, identity, secrets, observability, and scaling.
• Work directly with customer infrastructure, platform, security, networking, application, and operations teams.
• Translate customer security, reliability, compliance, and operational requirements into practical deployment architectures.
• Diagnose complex 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 a clear customer deployment plan and communicate progress, decisions, changes, risks, blockers, and resolution paths.
• Drive technical issues to closure across customer teams and Dynamo AI’s 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.

Required Qualifications

• 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 personally deploying and operating production applications on Kubernetes.
• Experience configuring and troubleshooting Kubernetes components such as 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, Kustomize, or equivalent tooling.
• Strong debugging skills across containers, distributed services, networking, logs, metrics, authentication, storage, and service dependencies.
• Experience designing or operating systems that meet production requirements for reliability, scalability, security, monitoring, upgrades, and incident response.
• Ability to independently own a complex customer deployment and drive ambiguous technical problems to resolution.
• Ability to manage deployment plans, dependencies, risks, customer actions, milestones, and operational readiness.
• Strong written and verbal communication skills, including the ability to explain technical decisions and tradeoffs to both engineers and non-engineering stakeholders.

Experience merely deploying applications onto a Kubernetes platform managed entirely by another team is not sufficient. Candidates should have direct experience diagnosing and resolving Kubernetes and infrastructure-level deployment issues.

Preferred Qualifications

• Experience deploying enterprise software into customer-owned cloud, private-cloud, hybrid, or on-premises environments.
• Experience working with regulated or security-sensitive organizations such as financial institutions, healthcare companies, government agencies, or large enterprises.
• 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 their 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.

Why This Role

Help Enterprises Deploy AI Responsibly

You will work directly with organizations deploying AI into critical real-world operations, helping ensure systems are reliable, secure, observable, scalable, and production-ready.

Work on Real Deployment Challenges

This role is deeply focused on practical deployment and operationalization. You will help customers navigate the technical and organizational realities of enterprise AI adoption.

Operate at the Intersection of AI and Enterprise Systems

You will gain exposure to a wide range of enterprise architectures, governance models, and operational workflows across regulated industries.

Shape How Enterprises Operationalize AI

Your work will directly influence how organizations safely deploy AI systems at scale, while shaping Dynamo AI’s deployment methodologies and product evolution.