MLOps Engineer

What an MLOps engineer works on

• Package models into reproducible images and promote them through staging with automated checks
• Build CI/CD for training and serving so a merge can ship a model without a weekend fire drill
• Run serving on Kubernetes or managed endpoints and keep latency, errors and cost in view
• Wire drift, data quality and performance alerts so the ML team hears about problems before users do
• Own the model registry, feature store hooks and the path from experiment to canary to full traffic
• Automate retraining jobs when labels or traffic patterns change, with clear rollback steps
• Partner with ML engineers and platform teams on GPU capacity, secrets and access controls
• Codify promotion criteria so a model cannot reach production without tests, owners and a rollback path
• Track cost per prediction and kill idle endpoints that waste GPU budget

MLOps skills to test

Skill	Signal of real depth
Model deployment	Ships a model behind an API with health checks, version pins and a documented rollback
CI/CD for ML	Separates code tests, data checks and model evals, and blocks a bad promote
Containers and orchestration	Reads Kubernetes events, tunes resources and explains why a pod is crashing
Observability	Tracks latency, error rate and prediction drift with alerts a human can act on
Infrastructure as code	Provisions serving and training infra with Terraform or the cloud's native tools
Collaboration	Writes runbooks ML engineers actually use during an incident

Interview questions and how Exordiom vets

1. A canary model spikes tail latency. What do you check first?
2. Walk us through how you promote a model from staging to 100% traffic.
3. How do you detect data drift when labels arrive days late?
4. Show a CI pipeline you built for models and what each gate blocks.
5. How would you cut GPU spend without freezing experiments?
6. Tell us about a production ML outage you led through.

Red flags: deploys with no rollback, metrics that only live in a notebook, no habit of documenting runbooks, and treating serving as someone else's problem.

Candidates stay in their current ML platform jobs until you hire them, so there's nobody sitting on a bench. Screening starts with an AI interview on deploy, registries and drift, then live rounds on judgment under production pressure. After day one, Hypercare runs two early check-ins and then a monthly cadence from week 6.

India vs Philippines for MLOps engineers

As of Nov 5, 2026, India shows the deeper MLOps pool: Smartsheet is hiring senior AI/ML Ops engineers in Bangalore, Sarvam wants an ML Ops engineer in Delhi, Aptiv is staffing AI/ML infrastructure in Bangalore, and Intel and Starburst list AI platform roles in India. Lightning AI lists an AI platform support role covering the Philippines. In both markets your MLOps engineer works your full US workday.