MLOps Engineer Resume Keywords (2026): 60+ AI Infrastructure Skills
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🚨 The 'Model is stuck in a notebook' problem.
In 2026, companies have thousands of AI models, but only 15% ever make it to production. The bottleneck isn't Data Science; it's MLOps. Companies are paying massive premiums for engineers who can take a model from a Jupyter Notebook to a scalable API.
Why MLOps Keywords Matter in 2026
MLOps (Machine Learning Operations) is the intersection of Data Science, DevOps, and Data Engineering. It is the "plumbing" of the AI revolution.
Recruiters are looking for a very specific stack. If you put "Machine Learning" on your resume, you look like a Scientist. If you put "Automated Retraining Pipeline" on your resume, you look like an Engineer. The difference is often $50k in salary.
This guide covers 60+ essential resume keywords for MLOps Engineers in 2026, categorized by infrastructure, lifecycle, and observability, to help you prove you can build production-grade AI systems.
Table of Contents
- What Are MLOps Keywords?
- Infrastructure & Orchestration
- Model Lifecycle & Deployment
- Monitoring & Observability
- Data Engineering for MLOps
- Security & Governance
- Resume Examples (DevOps vs. MLOps)
- FAQ
What Are MLOps Keywords?
MLOps keywords describe the tools and processes used to manage the Machine Learning Lifecycle (MDLC). They signal that you understand the unique challenges of versioning data, not just code.
ATS software scans for:
- Concepts: CI/CD for ML (CT/CD), Reproducibility, Scalability, High Availability.
- Tools: MLflow, Kubeflow, TFX, Seldon Core, Ray Serve.
- Actions: Orchestrate, Deploy, Monitor, Version, Serve, Retrain.
If you have a traditional ops background, review our DevOps Engineer Resume Keywords guide to see where your containerization skills overlap.
Infrastructure & Orchestration
Models don't run on air; they run on heavy metal (or virtual metal). This is where MLOps looks most like Cloud Engineering.
| Category | Keywords |
|---|---|
| Compute | Kubernetes (K8s), Docker, EKS (Elastic Kubernetes Service), GKE (Google Kubernetes Engine), Lambda/Serverless, GPU Acceleration (CUDA/NVIDIA), Ray, Slurm, Spot Instances |
| IaC | Terraform, Ansible, CloudFormation, Infrastructure as Code, Helm Charts, Pulumi, GitOps (ArgoCD) |
| Cloud | AWS SageMaker, Vertex AI (GCP), Azure AI Studio, Databricks, Hybrid Cloud, Multi-Cloud |
Pro Tip: "Kubernetes" is good. "Kubeflow pipelines on EKS" is better. Context is king. Mentioning GPU Optimization shows you care about cost, which hiring managers love.
Model Lifecycle & Deployment
This is the core of the job: moving code to prod. It differentiates you from a Data Scientist who stops at the .ipynb file.
| Category | Keywords |
|---|---|
| Tracking | MLflow, Weights & Biases (W&B), Comet.ml, Experiment Tracking, Model Registry, Artifact Management, Metadata Store |
| Serving | TorchServe, TensorFlow Serving, Triton Inference Server, ONNX Runtime, REST API, gRPC, Edge Deployment, Quantization, Model Compression |
| Pipelines | CI/CD, GitHub Actions, GitLab CI, Argo Workflows, Airflow, Jenkins, TFX (TensorFlow Extended), Step Functions |
If you are working with strategy teams, check AI Consultant Keywords to understand the business goals behind these deployments.
Monitoring & Observability
An MLOps engineer's job doesn't end at deployment. You must ensure the model stays smart. This is the "Day 2" operations that separates juniors from seniors.
| Category | Keywords |
|---|---|
| Performance | Model Drift, Data Drift, Concept Drift, Latency, Throughput, Accuracy Degradation, Bias Detection, Outlier Detection |
| Tools | Prometheus, Grafana, Arize AI, Fiddler, WhyLabs, ELK Stack (Elasticsearch, Logstash, Kibana), CloudWatch, OpenTelemetry |
| Response | Automated Retraining, Rollback Strategies (Canary/Blue-Green/Shadow Mode), Alerting, Incident Response, A/B Testing |
Data Engineering for MLOps
You can't train a model without data. MLOps engineers often manage the Feature Store to ensure training-serving skew is minimized.
- Feature Stores: Feast, Tecton, SageMaker Feature Store.
- Data Versioning: DVC (Data Version Control), Pachyderm, Delta Lake, Lakehouse Architecture.
- Processing: Spark, Kafka, Ray Data, Pre-processing Pipelines, Batch Inference.
Security & Governance
With AI regulation tightening (EU AI Act), security is a massive priority.
- Model Security: Adversarial Attack Defense, Model Signing, Supply Chain Security.
- Access Control: RBAC, IAM Policies, VPC Peering, PrivateLink.
- Compliance: Audit Logs, Lineage Tracking, Reproducibility, GDPR, PII Redaction.
Resume Examples: DevOps vs. MLOps
See the difference between a generic infrastructure engineer and a specialized MLOps pro.
Example 1: Deployment
❌ Weak: "Used Jenkins to deploy software."
✅ Strong (Optimized): "Built a CI/CD pipeline using GitHub Actions and AWS SageMaker, reducing model deployment time from 2 weeks to 1 hour with fully automated Canary Deployments."
Example 2: Monitoring
❌ Weak: "Watched logs to make sure the app was running."
✅ Strong (Optimized): "Implemented real-time Model Monitoring using Prometheus and Grafana, detecting Data Drift in production features and triggering automated retraining workflows."
Example 3: Infrastructure
❌ Weak: "Managed servers for the data team."
✅ Strong (Optimized): "Architected a scalable Kubeflow cluster on EKS to support distributed training jobs, utilizing GPU slicing to reduce cloud compute costs by 40%."
Conclusion
The MLOps Engineer is the architect of the AI future. As models become more complex, the systems that run them must become more robust.
By auditing your resume and including these 60+ keywords, you position yourself as the critical link between the "Science" and the "Production."
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