decagon Platform Engineer Interview: Questions & Prep (2026)

Decagon builds autonomous AI agents for enterprise customer support, serving large businesses that need reliable, always-on conversation infrastructure. A Platform Engineer at Decagon owns the systems that power those agents: compute, networking, developer tooling, CI/CD pipelines, and the reliability layer that keeps SLAs intact when large volumes of customer conversations are flowing through.

As of July 2026, knok jobradar shows Decagon has 117 open roles across functions. In the broader Indian market, there are 204 Platform Engineer openings, with Bangalore the clear hub at 29 listings, followed by Delhi (12) and Pune (10). The role sits at the crossroads of DevOps, SRE, and backend infrastructure, and Decagon's AI-native context means platform engineers also work closely with ML teams on inference pipelines and model deployment.

Candidates report the process typically covers a recruiter screen, one or two technical rounds focused on system design and infrastructure, and a final round that includes a values or leadership discussion. Interviewers at Decagon are known to value depth and honesty. Saying 'I do not know, but here is how I would find out' typically lands better than a confident but vague answer.

The following questions are commonly encountered in Platform Engineer interviews at AI-native product companies. They reflect what Decagon interviewers typically probe, based on candidate reports and the nature of the role.

1. Walk us through a CI/CD pipeline you designed or significantly improved. What were the biggest bottlenecks you fixed?
2. How would you architect a multi-tenant infrastructure that keeps enterprise clients isolated while staying operationally simple?
3. Decagon's agents process real-time customer conversations. How would you design a system to absorb sudden traffic spikes without dropping requests?
4. Describe your hands-on experience with Kubernetes. How have you managed autoscaling, resource quotas, and cluster health in production?
5. How do you approach observability in a distributed system? Walk us through a real metrics, logging, and tracing stack you have worked with.
6. Tell us about a production incident you owned from detection to resolution. What did you do, and what did you change afterwards?
7. How would you manage secrets and access control for a platform serving many enterprise tenants with different security policies?
8. What is your experience with Infrastructure as Code? Describe a project where it made a real difference to how your team operated.
9. AI inference can be compute-heavy and spiky. How would you optimize resource utilization for an LLM serving layer?
10. How do you build a self-serve developer platform so ML and product engineers can deploy and monitor workloads without depending on the platform team for every step?
11. Describe how you have handled database reliability, whether through replication, caching, or another approach, at a previous company.
12. Tell us about a time you cut infrastructure costs meaningfully. What did you change, and how did you protect reliability in the process?

Q: Walk us through a CI/CD pipeline you built or improved.

*Situation:* At my previous company, each service had its own manual deployment script. Releases were slow and required a senior engineer to supervise every push to production.

*Task:* I was asked to design and roll out a unified CI/CD system that any engineer could trigger safely and confidently.

*Action:* I introduced GitHub Actions with environment-specific approval gates. I built a shared pipeline library covering build, test, Docker image publish, and Helm-based deployment to a Kubernetes cluster. I added automated rollback triggered by failed health checks, and Slack notifications for every deployment event. I piloted with two teams before rolling out to the whole organisation.

*Result:* Deployment time fell from hours to minutes. Engineers could ship independently without ops involvement, and rollback became fast enough that on-call engineers stopped dreading production incidents.

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Q: Tell us about a production incident you owned end-to-end.

*Situation:* Our primary database cluster started throwing intermittent timeouts during peak hours. Customer-facing APIs were degraded and alerts were firing across multiple services.

*Task:* As the on-call platform engineer, I had to identify the root cause, restore service, and prevent recurrence.

*Action:* I started with slow query logs and found that a single analytics job was holding row-level locks during business hours. I killed the job immediately to restore service, then worked with the data team to move it to a nightly window. I added lock-wait timeout alerts and wrote a runbook so the next on-call engineer would know exactly what to check.

*Result:* Service was restored quickly. We had no recurrence in the months that followed, and the incident became a reference case for our runbook process after outages.

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Q: How have you handled multi-tenancy in infrastructure?

*Situation:* We were onboarding enterprise clients who each had strict data-isolation requirements. Our existing infrastructure was designed for a single tenant with no isolation boundaries.

*Task:* I was responsible for designing a multi-tenant model that kept client data and compute separate without multiplying operational overhead.

*Action:* I introduced namespace-level isolation in Kubernetes with separate service accounts and network policies per tenant. Secrets were scoped per tenant in a centralised vault with audit logging enabled. I also built a lightweight onboarding automation so adding a new tenant required a single configuration change rather than a manual sequence of steps.

*Result:* We onboarded new enterprise clients faster than before, passed security reviews from multiple large clients, and the platform team did not need to scale headcount proportionally with tenant growth.

STAR (Situation, Task, Action, Result) is the backbone for all behavioural and past-experience questions. Every answer should land on a concrete result, even if the result is 'we learned X and changed Y.' Avoid vague endings like 'it went well.'

For system design questions, use a four-part structure. Start with clarifying questions (scale, consistency needs, latency targets). Then walk through a high-level architecture. Zoom into the components that matter most for the given scenario. Close with trade-offs you made and what you would revisit with more time. At Decagon, interviewers typically want to see that you think in trade-offs, not that you have a memorised blueprint.

For debugging and incident questions, follow a detect-isolate-fix-prevent flow. Show that you start from signals (metrics, logs, traces), narrow down systematically, resolve with the least risky action first, and then close the loop with a process change or runbook update.

For open-ended 'how would you' questions, frame your answer as: 'Here is my first-pass approach, here is where I am uncertain, and here is how I would validate my assumptions.' This signals engineering maturity more reliably than a confident answer that skips over unknowns.

Depth over breadth. Decagon's platform team is small relative to the scope of what they run. Interviewers want engineers who can go deep on a problem, not someone who lists every tool in the ecosystem without real experience behind any of them.

Ownership mentality. The team culture typically rewards engineers who treat the platform as their product. Expect questions that probe whether you follow up on incidents, write runbooks, and improve systems after they break, not just during the crisis.

ML-infrastructure awareness. Because Decagon's core product relies on large language models, platform engineers are expected to understand at least the basics of inference infrastructure, GPU resource management, and model serving. You do not need to be an ML engineer, but you should understand the compute and latency characteristics of LLM workloads.

Clear, structured communication. Candidates report that interviewers at Decagon appreciate engineers who think out loud, state their assumptions, and ask clarifying questions before diving into an answer. A visible, honest thinking process is valued over silence followed by a polished response.

Security and compliance awareness. Enterprise clients come with security requirements. Interviewers want to see that you naturally think about access control, audit trails, and data isolation as part of your default design approach, not as an afterthought bolted on at the end.

Week one: solidify your infrastructure fundamentals. Review Kubernetes core concepts (pods, deployments, services, ingress, RBAC, network policies). Practice drawing architecture diagrams for common patterns: message queues, caching layers, read replicas, and stateless compute. If you have gaps, pick one real project you have worked on and be ready to walk through every layer of its infrastructure in detail.

Week two: go deep on system design for AI platforms. Study how LLM inference stacks are typically structured: request routing, batching, GPU allocation, and serving frameworks. You do not need to write inference code, but you should be able to discuss the infrastructure concerns clearly. Practice designing a multi-tenant platform on a whiteboard or in a notebook, focusing on isolation, autoscaling, and cost control.

Week three: practise behavioural answers and research Decagon. Map your past incidents, cost-saving wins, and platform improvements to the STAR format. Read any public engineering content from Decagon and study their product closely so your examples connect to their context. Ask a peer to give you a mock interview covering at least one system design and two behavioural questions. Confirm round formats with your recruiter before each stage, since structures can vary.

While you are deep in prep mode, knok checks 150+ job sites nightly, applies to roles that match your resume, and messages HR for you, so you can stay focused on interview prep rather than chasing new listings.

Naming tools without showing depth. Many candidates list Terraform, Kubernetes, and Prometheus in one breath but cannot explain a real trade-off they made using any of them. Interviewers notice this very quickly.

Skipping clarifying questions in system design. Jumping straight into architecture without asking about scale, latency requirements, or consistency needs signals that you design for imaginary workloads rather than real ones.

Giving vague incident answers. Saying 'we had an outage and fixed it' is not an answer. Interviewers want the actual signals you saw, the steps you took, and what you changed to prevent recurrence. Prepare two or three detailed incident stories before your interview.

Ignoring the multi-tenancy angle. Decagon serves enterprise clients who care deeply about isolation and compliance. Candidates who design single-tenant systems by default and only add isolation as an afterthought typically lose marks on design questions.

Over-focusing on tooling and under-focusing on outcomes. Platform engineers are hired to make the rest of the engineering organisation move faster and more safely. Frame your experience around what improved for your teams, not just which tools you deployed.

Not asking questions at the end. Interviewers typically leave time for your questions. Candidates who ask nothing signal low interest. Prepare two or three specific questions about the team's roadmap, on-call culture, or how platform priorities are set relative to product work.