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Forward Deployed Engineer

AI Summary
Kong is hiring a senior Forward Deployed Engineer to deliver production-grade API and AI connectivity solutions for enterprise customers. The role combines hands-on software engineering, platform architecture, cloud-native infrastructure, and professional services delivery. The engineer will build automation, migration tools, integrations, CI/CD workflows, and Kubernetes-based tooling while working directly with customer technical and executive stakeholders. Success requires deep experience in API management, distributed systems, public cloud, Infrastructure as Code, and ambiguous customer environments.
Remote from
USA
Salary
USD 192,010–274,300 / yr
Employment
Full Time
Experience
Director
Published
Apply before
13 Dec 2026
Listing views
34
Application actions
1
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Opportunity details

About this role.

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If you don’t think you meet all of the criteria below but are still interested in the job, please apply. Nobody checks every box – we’re looking for candidates that are particularly strong in a few areas, and have some interest and capabilities in others.

About the Role:

We are seeking a Forward Deployed Engineer (FDE) to join Kong’s growing Professional Services organization. This is a highly strategic, customer-facing engineering role focused on helping enterprise customers accelerate adoption of Kong’s API and AI Connectivity platform through hands-on implementation, automation, migration tooling, and solution engineering.

As an FDE, you will work directly alongside customer engineering teams to solve complex technical challenges, accelerate migrations, build reusable automation, and deliver production-grade solutions in real-world enterprise environments. You will operate at the intersection of software engineering, architecture, platform engineering, and customer delivery.

This role is ideal for engineers who enjoy building, coding, solving ambiguous problems, and working directly with customers on mission-critical initiatives involving APIs, AI gateways, service connectivity, modernization, and cloud-native platforms.

You will collaborate closely with Solutions Architects, Engagement Managers, Product Engineering, Customer Success, and Product teams to ensure successful customer outcomes while also influencing Kong’s product direction based on field experience.

Working at Kong is a unique opportunity to make a massive impact on Fortune 50/500 customers as they modernize mission-critical systems for the AI era.

What’s in it for you?

Impact

Work directly with some of the world’s largest enterprises to modernize API infrastructure and accelerate AI adoption through Kong’s connectivity platform.

Growth

Accelerate your technical and professional growth through exposure to cutting-edge cloud-native technologies, distributed systems, AI connectivity, automation frameworks, and large-scale enterprise architectures.

Opportunity

Be part of a rapidly growing Professional Services organization that is shaping how enterprises adopt APIs, AI gateways, and modern connectivity platforms globally.

What You’ll Do:

  • Partner directly with enterprise customers to design, build, and implement Kong-based solutions across cloud, hybrid, and on-premise environments.

  • Lead hands-on implementation efforts for API gateways, AI gateways, service mesh, developer portals, and related connectivity technologies.

  • Build automation frameworks, migration tooling, custom integrations, and reusable accelerators to simplify customer adoption and modernization efforts.

  • Drive migration initiatives from legacy API management platforms to Kong.

  • Work closely with customer developers, platform teams, architects, security teams, and executives to solve complex technical and operational challenges.

  • Develop production-grade code, scripts, plugins, CI/CD integrations, Kubernetes automation, and infrastructure tooling as part of customer engagements.

  • Collaborate with Kong Product and Engineering teams to influence roadmap priorities based on real-world customer feedback and implementation learnings.

  • Create reusable best practices, technical assets, reference architectures, documentation, and design patterns to scale delivery excellence across the organization.

  • Support strategic customer initiatives involving AI connectivity, governance, platform modernization, and cloud-native transformation.

What You’ll Bring:

Experience

  • 8+ years in customer-facing engineering, platform engineering, professional services, site reliability engineering, or software engineering roles.

  • Strong hands-on software engineering and implementation experience.

  • Experience with API gateways, API management, integrations, or distributed systems platforms.

  • Strong Kubernetes and cloud-native infrastructure expertise.

  • Experience building automation frameworks, deployment tooling, CI/CD pipelines, or migration tooling.

  • Strong proficiency in Linux, networking, cloud infrastructure, and DevOps practices.

  • Experience with major cloud providers including AWS, Azure, or GCP.

  • Programming experience in languages such as Python, Go, Java, JavaScript, or Lua.

  • Experience with Infrastructure-as-Code and automation tooling such as Terraform, Helm, Ansible, or GitOps frameworks.

  • Prior experience working in API Management, Open Source, Middleware, SaaS, or Infrastructure software companies is highly preferred.

Preferred Attributes:

  • Strong builder mindset with the ability to solve complex technical problems with minimal supervision.

  • Comfortable operating in ambiguous, fast-moving enterprise environments.

  • Passion for working directly with customers and delivering measurable business outcomes.

  • Excellent communication and collaboration skills across technical and executive audiences.

  • Highly organized with strong prioritization and execution skills.

  • Passion for innovation, modern infrastructure, APIs, AI, and cloud-native technologies.

  • Team-oriented with a strong sense of ownership and accountability.

#LI-AW1

About Kong:

Kong Inc., the AI Connectivity Company, is building the connectivity layer of AI. Trusted by the Fortune 500® and AI-native startups alike, Kong’s unified API and AI platform enables organizations to secure, manage, accelerate, govern, and monetize the flow of intelligence across APIs and AI traffic — on any model, any cloud. For more information, visit www.konghq.com.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a senior, customer-facing engineering role requiring at least eight years of relevant experience and broad technical depth across APIs, Kubernetes, cloud infrastructure, automation, and migrations. It also demands independent delivery of high-stakes solutions in complex enterprise environments.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$233,155
US market range (per year)$190k–$285k
AI insightThe employer explicitly provides a yearly base-pay range of USD 192,010 to USD 274,300, with a midpoint of USD 233,155. The estimated US market range for a senior customer-facing platform and cloud-native engineering specialist is USD 190,000 to USD 285,000 per year.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe a complex migration from a legacy API management platform to a modern API gateway.

I would begin with an inventory of APIs, traffic patterns, policies, authentication methods, dependencies, and operational requirements. I would define a phased migration plan with automated configuration conversion, test environments, observability baselines, rollback procedures, and progressive traffic cutovers. Throughout the engagement, I would work closely with customer teams to validate functional, security, and performance outcomes.

How would you design Kubernetes automation for a multi-environment API platform deployment?

I would use version-controlled declarative configuration, reusable Helm charts or Kustomize overlays, Infrastructure as Code for cloud resources, and GitOps-driven promotion across environments. The solution would include secrets management, policy controls, health checks, monitoring, automated rollback, and clear operational documentation. I would also ensure the design supports customer-specific governance and tenancy needs.

How do you approach ambiguous technical problems during a customer engagement?

I first clarify the desired business outcome, technical constraints, stakeholders, and measurable success criteria. I break the problem into validated hypotheses, create a small proof of value where appropriate, and communicate risks and trade-offs early. This lets the team make progress while preserving alignment as new information emerges.

What practices would you use to secure and govern API and AI gateway traffic?

I would apply strong authentication and authorization, rate limiting, traffic policies, encryption, audit logging, and centralized observability. For AI traffic, I would additionally consider model access controls, prompt and response governance, data handling requirements, and usage monitoring. Controls should be automated, consistently applied, and tailored to the customer's regulatory and operational requirements.

How do you balance reusable engineering assets with customer-specific delivery needs?

I identify common patterns early and build modular accelerators, templates, reference architectures, and automation components that can be parameterized per customer. Customer-specific logic is isolated behind clear interfaces rather than embedded into the reusable core. I document implementation lessons so successful approaches improve future delivery engagements and product feedback.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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