baseten DevOps Engineer Interview: Questions, Experience & Prep (2026)

Baseten is a platform for deploying and serving machine learning models at production scale, used by engineering teams who need reliable, low-latency inference infrastructure. With 74 open roles currently listed, the company is actively hiring, and DevOps Engineers sit at the heart of that growth. The role combines cloud-native infrastructure work, Kubernetes orchestration, and the specific demands of ML model serving pipelines.

Across India, knok jobradar tracked 811 DevOps Engineer openings as of July 2026. Bangalore leads with 187 roles, followed by Delhi (40), Pune (37), Hyderabad (28), Chennai (13), and Mumbai (11). Salary bands for DevOps in India run from 6-12 LPA at entry level (0-2 years) to 15-28 LPA at mid level (3-5 years), 30-50 LPA for senior engineers (6-9 years), and 45-70+ LPA for Lead or Staff engineers. Baseten does not publicly disclose its compensation ranges, but Glassdoor and levels.fyi show that ML-infrastructure-focused companies commonly offer pay at or above the senior end of market bands for candidates with strong Kubernetes and observability backgrounds.

The interview process at Baseten typically includes a recruiter or hiring-manager intro screen, one or two technical rounds covering infrastructure design and systems thinking, and a values or team-fit conversation. Candidates report the process moving at a reasonable pace once it begins, usually completing within a few weeks of first contact.

These questions come up repeatedly in Baseten DevOps interviews, based on what candidates report and the nature of the product (ML model serving at scale):

1. Walk me through how you have designed and operated Kubernetes clusters for high-throughput, latency-sensitive workloads.
2. How would you build a CI/CD pipeline for containerised ML model deployments, from code commit to production rollout?
3. Describe your approach to auto-scaling for unpredictable inference traffic. What signals do you use, and how do you avoid over-provisioning idle compute?
4. How do you set up observability for a model-serving environment? What metrics, logs, and traces matter most to you and why?
5. How would you design a zero-downtime deployment strategy for a live model endpoint used by paying customers?
6. Tell me about a time you significantly reduced infrastructure costs without hurting reliability.
7. How do you approach secrets management and security hardening in a cloud-native stack?
8. What is your hands-on experience with GPU infrastructure? How do you schedule and optimise GPU workloads on Kubernetes?
9. A recent deployment is causing a spike in tail latency on a serving endpoint. Walk me through how you would diagnose and resolve it.
10. How would you manage infrastructure-as-code across multiple environments or cloud regions?
11. How do you ensure that model rollbacks happen quickly and safely when a bad version reaches production?
12. Describe your on-call incident response process and how you structure post-mortems to prevent repeat incidents.

Q: Describe your approach to auto-scaling for unpredictable ML inference traffic.

*Situation:* At my previous company, we ran a real-time inference API whose traffic could spike sharply during business hours with almost no warning.

*Task:* I was responsible for designing an auto-scaling setup that responded fast enough to absorb spikes without leaving idle compute running for hours at a time.

*Action:* I configured the Kubernetes Horizontal Pod Autoscaler using custom metrics from our inference queue depth rather than CPU alone, since CPU was a poor predictor of actual request pressure for this workload. I also pre-warmed a small pool of standby replicas during expected peak windows and enabled node auto-provisioning so new nodes could join when pod demand crossed a set threshold. I worked with the ML team to cut model cold-start time by baking weights into the container image rather than downloading them at startup.

*Result:* Median request wait time during traffic peaks dropped noticeably, and idle compute spend fell according to our cloud cost dashboard. The team moved from a reactive posture, where spikes caused queuing, to a proactive capacity model.

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Q: Tell me about a time you reduced infrastructure costs without sacrificing reliability.

*Situation:* Our staging environment was nearly a full mirror of production and ran around the clock, even when no one was actively using it.

*Task:* I was asked to find meaningful savings in non-production environments without touching the production setup.

*Action:* I audited resource usage over a full month and confirmed that staging sat largely idle outside business hours. I introduced Kubernetes CronJobs that scaled non-critical staging workloads down to zero each evening and back up each morning. I also right-sized node pools by switching from a single large instance type to a mix of spot and on-demand nodes, using pod disruption budgets to protect any jobs that had to keep running.

*Result:* The monthly cloud bill for staging fell by a meaningful amount as reflected in our internal cost reports, with no staging outages during the following quarter. Leadership used the same pattern across other non-production environments.

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Q: How would you design a zero-downtime deployment strategy for a model serving endpoint?

*Situation:* We were rolling out a new version of a computer-vision model that backed a customer-facing feature. Any downtime would directly affect end users.

*Task:* I had to design a deployment process that kept the old version serving traffic until the new one was verified stable.

*Action:* I implemented a blue-green deployment using Kubernetes Services and an Ingress controller to split traffic between the old version (blue) and the new version (green). Green initially received a small slice of live traffic while blue handled the majority. I set up Prometheus alerting rules tied to latency and error-rate metrics that would automatically cut all traffic back to blue if green showed degradation, without requiring manual intervention.

*Result:* The rollout completed with no customer-facing downtime. The team adopted this pattern as the standard process for all subsequent model releases, and on-call burden during deployment windows dropped significantly.

For system design questions (for example, 'how would you build X'): Start by clarifying the requirements before drawing any architecture. Walk through components in a logical order, explain the trade-offs at each decision point, and finish by describing how you would monitor the system in production. At Baseten, interviewers care about your reasoning process, not just the final design.

For behavioral questions (for example, 'tell me about a time...'): Use the STAR structure: Situation (brief context), Task (what you personally owned), Action (what you specifically did, not what 'the team' did), Result (a concrete outcome with a metric or clear business impact). Keep Situation and Task short. Most of your answer should be in the Action section.

For troubleshooting questions (for example, 'a deployment just broke something'): Lead with your first priority, which is stabilising the system before finding root cause. Describe how you gather signals: logs, metrics, traces, and recent changes in the deployment history. Explain how you isolate the fault to a specific component. Close with the fix and what you would add to prevent recurrence. This structure shows incident-response maturity.

For cost or efficiency questions: Frame your answer around the trade-off between cost, reliability, and delivery speed. Show that you can optimise one dimension without blindly sacrificing the others. Baseten interviewers want engineers who treat cost and reliability as co-equal concerns, not opposites.

Baseten builds infrastructure for ML teams who cannot afford unreliable or slow model serving. Interviewers look for a few specific qualities:

Deep Kubernetes knowledge. Not just familiarity, but a genuine understanding of scheduling, resource limits, node affinity, pod disruption budgets, and operators. Candidates who can explain why they made specific Kubernetes choices, and what the trade-offs were, consistently stand out.

ML infrastructure awareness. You do not need to be an ML engineer, but you should understand concepts like model warm-up, GPU scheduling, inference batching, and why these make ML serving different from a typical web API. Baseten's product is built specifically for this problem space.

Cost and reliability thinking together. Interviewers want engineers who treat these as complementary, not opposing. Show that you can make infrastructure more efficient without making it fragile, and that you consider both dimensions when making decisions.

Incident-response maturity. Candidates report that Baseten asks detailed questions about how you handle on-call rotations, write post-mortems, and prevent repeat incidents. Clear, structured answers here carry significant weight in the evaluation.

Communication across teams. You will likely work alongside ML engineers and product teams who do not have deep infrastructure backgrounds. The ability to explain system behaviour or a trade-off in plain terms is a real differentiator at a company of this stage.

Week 1: Kubernetes and container fundamentals. Review how deployments, services, Ingress, Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and node auto-provisioning work together. Practice explaining each component out loud as if teaching a colleague. Make sure you can discuss pod scheduling, resource requests and limits, and how Kubernetes handles node failures gracefully.

Week 2: Observability and CI/CD for ML workloads. Set up a small Prometheus and Grafana stack locally if you have not recently. Review how you would instrument a model-serving API. Study canary and blue-green deployments, and GitOps patterns using ArgoCD or Flux. Read about GPU node pools and how GPU resource requests work in Kubernetes.

Week 3: Behavioural prep and mock interviews. Map your past experience to the core question themes: scaling, cost reduction, incident response, and cross-team collaboration. Write out STAR stories for each theme and practice saying them aloud to catch filler words and vague claims. If possible, do at least one mock interview with a peer who can give honest feedback.

Day before: Review Baseten's public engineering content and product documentation so you understand the problems they are solving. Prepare two or three thoughtful questions for your interviewer about the team's current infrastructure challenges.

knok checks 150+ job sites nightly, applies to DevOps roles that match your resume, and messages HR for you, so your job search keeps running in the background while you focus on interview prep.

Treating it like a generic SRE interview. Baseten is not a generic web company. Candidates who give textbook answers without connecting them to ML serving specifics (model latency, GPU utilisation, inference throughput) often do not advance past the technical round.

Vague STAR answers. Saying 'we improved performance' without a concrete metric or clear business outcome sounds thin. Even if you cannot share exact figures, describe the kind of impact it had: reduced the on-call burden, cut the cloud bill by a meaningful amount, or eliminated a whole class of production incidents.

Skipping the trade-off discussion. When asked about a system design, jumping to a single solution without discussing alternatives signals shallow thinking. Always explain what you considered, why you chose your approach, and what risks you would monitor for.

Ignoring GPU infrastructure. Many DevOps candidates skip this topic entirely. Baseten's core product depends on GPU-accelerated inference. Even a basic understanding of GPU node pools, Kubernetes device plugins, and scheduling constraints will set you apart from candidates who have never touched this area.

Not preparing questions for your interviewer. Baseten interviews typically leave time for your questions. Candidates who respond with 'I think I am good' miss a real opportunity to show genuine interest in the team's problems and to assess whether the role is a fit.