MLOps Engineer

What an MLOps Engineer does

MLOps Engineers build the systems that take machine learning from notebooks to reliable, secure, and observable production services. You own the lifecycle: data pipelines, training and evaluation, packaging, deployment, monitoring, and continuous improvement.

• Automate training and batch pipelines (feature engineering, training, validation)
• Package and serve models (APIs, batch jobs, streaming)
• Manage registries, artifacts, and versioning across data and models
• Run CI/CD for ML (tests, reproducible builds, gated releases)
• Operate infrastructure (containers, Kubernetes, workflow engines)
• Monitor performance, drift, and business impact; roll back safely
• Handle security, privacy, and compliance requirements

Typical deliverables

• Production-grade model API or batch scoring pipeline
• Automated training pipeline with validation gates
• Model registry entries with lineage, metrics, and model cards
• CI/CD pipelines (tests, security scans, deployment stages)
• Monitoring dashboards and actionable alerts (latency, accuracy, drift)
• Runbooks and SLOs for model services

Hiring expectations by level

Junior

• Can containerize apps and write basic pipelines under guidance
• Understands data/model versioning concepts
• Writes unit tests for feature code and simple model checks
• Operates within prebuilt CI/CD templates

Mid-level

• Designs and maintains training and serving pipelines end-to-end
• Implements rollout strategies (canary, blue/green) and observability
• Owns cost, reliability, and performance trade-offs
• Champions reproducibility and governance practices

Senior

• Leads platform design (feature store, registry, workflow orchestration)
• Sets SLOs, incident response, and lifecycle governance
• Partners with DS/Platform/Sec to standardize patterns and templates
• Mentors teams and scales practices across products

Salary ranges

• Junior: $70k–110k
• Mid-level: $110k–160k
• Senior: $150k–220k+
• Staff/Lead: $200k–300k+ (often includes equity)

Varies by country/company; treat as rough ranges.

Where you can work

• Industries: fintech, e-commerce, healthtech, SaaS, gaming, logistics, cybersecurity, adtech
• Teams: data platform, ML platform, ML engineering, product ML, fraud/risk, personalization
• Company sizes: startups (wear many hats) to enterprises (own a platform area)

Who this is for

• Engineers who enjoy systems thinking and automation
• Data scientists who like productionizing and operating models
• DevOps/SREs curious about ML-specific workflows and telemetry

Prerequisites

• Comfortable with Python and command line basics
• Familiarity with containers (Docker) and Git
• Basic understanding of ML workflows (training, validation, inference)

Learning path

1. Foundations — MLOps principles, reproducibility, environments, artifact lineage.
   Mini task: Package a simple sklearn model and save metrics and artifacts with clear folder/version naming.
2. Pipelines — Training and batch pipelines; orchestrate on a workflow engine.
   Mini task: Build a DAG with steps: ingest → feature → train → evaluate → register.
3. Serving — Package models for real-time/batch; set rollouts and SLOs.
   Mini task: Expose a REST endpoint for inference with health/readiness probes.
4. Versioning & Registry — Track data/model versions and lineage; promote models across stages.
   Mini task: Create a model entry with stage transitions (Staging → Production).
5. Infra & Orchestration — Containers, Kubernetes, and workflow engines in practice.
   Mini task: Run your training job on Kubernetes with resource requests/limits.
6. Monitoring — App metrics, business KPIs, data drift, prediction quality.
   Mini task: Add latency/throughput metrics and an alert on drift.
7. Security & Compliance — Secrets, PII, audit trails, reproducible releases.
   Mini task: Store secrets securely and add a model card with risk notes.

Skills map

Master these to be job-ready:

• MLOps Foundations — principles, lifecycle, reproducibility, governance
• ML Training and Batch Pipelines — data prep, training, evaluation DAGs
• Model Packaging and Serving — batch and online inference patterns
• Model Registry and Artifact Management — lineage, stages, metadata
• Feature Store Operations — reusable, versioned features and point-in-time correctness
• Data and Model Versioning — datasets, schemas, and model versioning strategies
• CI/CD for ML Systems — tests, checks, and gated, reproducible releases
• Containerization and Images — secure, slim, deterministic images
• Kubernetes for ML Workloads — scheduling, scaling, and GPU/CPU workloads
• Orchestration and Workflow Engines — Airflow/Prefect/Kubeflow patterns
• Observability and Monitoring — logs, metrics, traces, dashboards, alerts
• ML Specific Monitoring — drift, performance decay, data quality
• Security and Compliance for ML — secrets, PII, auditability, approvals

Interview preparation checklist

• Explain model deployment patterns (canary vs blue/green) and when to use each
• Walk through your pipeline DAG and failure handling strategy
• Show how you version data and models together and reproduce a past result
• Discuss monitoring: what metrics, thresholds, and rollback triggers
• Demonstrate CI/CD for ML with testing levels and promotion gates
• Describe security practices: secret management, PII, SBOM, dependency scans
• Prepare a concise incident postmortem with remediation and learnings

Practical projects for your portfolio

1. Fraud Detection API
   • Build: training pipeline (balanced sampling), model serving API with canary rollout
   • Monitor: precision/recall, latency, drift on transaction amount/location
   • Show: dashboards, rollback runbook, model card with ethics notes
2. Batch Demand Forecasting
   • Build: weekly batch pipeline with backfills and point-in-time features
   • Monitor: MAPE, data freshness, upstream schema changes
   • Show: lineage graph and cost optimization (spot instances)
3. Feature Store for Recommendations
   • Build: reusable user and item features with time travel
   • Monitor: feature quality and null rates; ensure training/serving feature parity
   • Show: feature governance (owners, SLAs)
4. Model Registry & Promotion
   • Build: registry with Staging/Prod stages; automated evaluation gates
   • Monitor: post-deploy accuracy and traffic split
   • Show: audit trail and reproducible promotion via CI
5. Observability Pack
   • Build: logs/metrics/traces for one training job and one API
   • Monitor: SLOs (latency, error rate) and alert routing
   • Show: incident simulation and recovery time

Common mistakes and how to avoid them

• No data lineage: always version data and record hashes and schemas
• Training-serving skew: enforce the same feature code in both paths
• No rollback plan: define clear automated rollback triggers and scripts
• Oversized images: use slim bases, multi-stage builds, and pin versions
• Alert noise: alert only on user-impacting SLOs and critical drifts
• Secret sprawl: use a secrets manager; never commit credentials

Next steps

Pick a skill to start in the Skills section below, build one mini project per week, and take the exam to check your readiness. Progress is saved for logged-in users; everyone can take the exam for free.