MLOps Engineer

About the role

An MLOps Engineer keeps machine learning systems running reliably in production. The role combines software reliability engineering with ML-specific concerns: model drift, retraining triggers, experiment reproducibility, and the operational debt that accumulates when data scientists ship notebooks without deployment infrastructure. You are the person who gets paged when a recommendation engine goes stale, when a fraud model starts flagging legitimate transactions at 3x normal rate, or when a training job silently finishes on corrupted data. The job is operationally intensive and rewards people who read logs for fun and can debug a misbehaving Kubernetes pod and a misbehaving gradient in the same afternoon.

What this role actually does

• Build and maintain CI/CD pipelines for ML models that run automated tests, performance checks, and data validation before any model reaches production.
• Set up model monitoring covering prediction drift, feature drift, and downstream business metrics with automated alerting.
• Operate retraining pipelines triggered by drift thresholds, data arrival schedules, or manual escalation.
• Manage model registries and deployment infrastructure, including canary rollout, shadow deployment, and rollback procedures.
• Debug production model failures by tracing issues from business metric degradation back to data pipeline, feature logic, or model artifact.
• Define and enforce data quality contracts so failures in upstream pipelines surface as explicit errors rather than silent model degradation.
• Work with data scientists and ML engineers to standardize experiment tracking, artifact versioning, and model handoff procedures.

An average week

• Review model monitoring dashboards for drift, latency regressions, and error-rate anomalies across all production models.
• Triage retraining alerts: determine if drift is real or caused by upstream data issues, then route accordingly.
• Ship one infrastructure improvement per week: tighter data validation, faster training pipelines, better alert thresholds.
• Run a weekly model health review with ML engineers covering SLA compliance, prediction volume, and any pending rollbacks.
• Write a brief post-mortem for any production model incident, including root cause and the infrastructure change that prevents recurrence.

Required skills

• Kubeflow Pipelines, Metaflow, or Apache Airflow for orchestrating multi-step ML workflows with retry logic and dependency management.
• MLflow or Weights & Biases for experiment tracking, model versioning, and artifact storage integrated into CI/CD workflows.
• Kubernetes: writing Deployment and CronJob manifests, reading pod logs, debugging OOMKilled training jobs, and managing GPU resource quotas.
• Model monitoring with Evidently AI, WhyLabs, or Arize for drift detection, data quality checks, and alerting on statistical threshold breaches.
• Docker: multi-stage builds for training containers and serving containers that keep image sizes small for fast cold starts.
• Prometheus and Grafana for instrumenting model servers with custom prediction-volume and latency metrics beyond standard SRE signals.
• Python scripting at the level of writing validators, data quality checks, and alerting integrations without needing ML Engineer help.
• Cloud ML platforms (SageMaker, Vertex AI, or AzureML) including managed training jobs, endpoint deployment, and model registry integration.

What differentiates strong candidates

• Terraform for provisioning ML infrastructure as code: training clusters, feature store backends, and serving endpoints.
• Kafka or Pub/Sub for streaming feature pipelines that feed low-latency online models.
• Great Expectations or Soda for declarative data quality contracts at the pipeline level.
• Chaos engineering applied to ML systems: deliberately injecting corrupted features or stale models to verify monitoring catches the failure.

Salary bands by experience

Source anchors: Levels.fyi 2025-2026 + Glassdoor public ranges. Total compensation varies by location, company, and negotiation.

Career ladder

1. MLOps Engineer (2-4 yrs): Build and maintain CI/CD pipelines, operate model monitoring, on-call for production incidents.
2. Senior MLOps Engineer (4-7 yrs): Platform architecture decisions, mentoring, retraining system design, incident leadership.
3. Staff MLOps Engineer (7-10 yrs): Cross-org ML infrastructure strategy, vendor evaluation, reliability standards.
4. ML Platform Engineering Manager (6+ yrs): Team leadership track: headcount, roadmap ownership, cross-functional stakeholder management.

Transition paths into this role

Recommended courses

• Designing Machine Learning Systems (Chip Huyen): Chip Huyen's chapters on deployment, monitoring, and data engineering are essential reading for MLOps Engineers who need to think about system design, not just tooling.
• Full Stack Deep Learning: Covers the operational side of ML including infrastructure, testing, and deployment workflows. The lab exercises on CI/CD for models are directly applicable.
• AI Engineering Mastery: Includes deployment and monitoring patterns for AI systems, relevant for MLOps Engineers supporting security-adjacent ML applications.

Companies that hire for this role

Google · Meta · Netflix · Airbnb · DoorDash · Databricks · DataRobot · Weights & Biases

DecipherU is not affiliated with, endorsed by, or sponsored by any company listed. Information is compiled from publicly available job postings for educational purposes.

Representative certifications

• AWS Certified Machine Learning Engineer - Associate (Amazon Web Services)
• Google Cloud Professional Machine Learning Engineer (Google Cloud)
• Certified Kubernetes Administrator (CKA) (Linux Foundation / CNCF)
• Machine Learning Engineering for Production (MLOps) Specialization (DeepLearning.AI / Coursera)

Verify current pricing, exam format, and requirements directly with the certifying organization before making decisions.

MLOps Engineer questions and answers