The simple definition

A Platform Engineer builds and runs the internal platform that software, data, and ML teams use to ship reliably: from code → to production, from data → to insights, from models → to production endpoints - while staying secure and observable.

That’s the textbook version.

The version that matches reality: platform engineering is DevOps done deliberately, with a clear “product” mindset. The “product” is the platform itself: paved roads, reusable building blocks, guardrails, golden paths, and automation that make the right way the easy way.

And in the last few years, that platform increasingly includes not just application infrastructure, but also data pipelines, ML training and deployment, and AI infrastructure (LLMs, vector databases, prompt management). Same principles, broader scope.

Platform Engineering vs DevOps

Where they overlap (a lot)

Platform Engineers and DevOps Engineers typically both end up being:

  • Masters of CI/CD: pipelines, environments, release strategies, artifact flows (whether that’s containers, Python wheels, or trained models).
  • Infrastructure-as-Code people: Terraform/OpenTofu, modules, state, multi-account / multi-env / multi-cloud patterns.
  • Cloud and Kubernetes builders: networking, IAM, clusters, node scaling, ingress, service-to-service wiring.
  • DevSecOps enforcers: policy-as-code, supply chain security, scanning, secrets, encryption, least privilege.
  • SRE-adjacent operators: reliability, incident response, SLOs, runbooks, performance, cost control.
  • Automation gremlins: everything repeatable, everything codified, minimal click-ops.
  • Data and ML infrastructure wranglers (increasingly): orchestrating data pipelines, provisioning GPU clusters, managing feature stores, and deploying models.

The difference that matters

The main distinction is not the tech. It’s the intent:

  • DevOps (as practised) often gets pulled into “help this team ship this thing now.”
  • Platform Engineering (done well) is “build the repeatable system so every team ships faster forever.”

So yes: same skills, same toolbelt. But platform engineering is DevOps with a product lens:

  • clear interfaces,
  • opinionated defaults,
  • self-service,
  • documentation,
  • user feedback,
  • and measurable outcomes (lead time down, failures down, developer experience up).

What “Platform Engineer” means in my day-to-day

In practice, I build platforms that answer these questions without drama:

1) “How do we ship software from code to production?”

  • CI/CD standards: GitHub Actions, build caching for speed, environments, approvals, provenance, and enforced linting.
  • Release patterns: trunk-based, feature flags, canary/blue-green where it makes sense.
  • Artifact lifecycle: versioning, digests, promotion (dev → stage → prod), rollback paths.

2) “How do we build infrastructure safely and repeatably?”

  • Terraform/OpenTofu modules that are readable, parameterised, and secure by default.
  • Multi-account / multi-environment patterns (separation of duties and blast radius control).
  • Encryption everywhere (in transit + at rest) with sensible KMS boundaries.
  • Tagging, naming, and governance that doesn’t make engineers cry.

3) “How do we stay secure without slowing down?”

  • Shift-left security: SAST, IaC scanning (Checkov), secret scanning, dependency scanning.
  • Supply chain hardening: SBOMs, signing (Cosign), provenance, controlled registries.
  • Identity-first design: least privilege IAM, OIDC federation, short-lived credentials.
  • Policy as guardrails: automated checks that fail fast before bad things reach prod.

4) “How do we run this stuff at 3am?”

  • Observability as a platform feature: logs, metrics, traces, dashboards, alerting.
  • Operational playbooks: runbooks, incident response, “what good looks like.”
  • SLO-ish thinking: reliability targets, error budgets, real feedback loops.
  • Cost and performance: scaling, right-sizing, autoscaling policies, avoiding surprise bills.

5) “How do we make this easy for developers?”

This is the part that separates “we run infra” from “we run a platform.”

  • Golden paths: “If you build a service this way, it deploys, scales, logs, and is compliant.”
  • Self-service: templates, scaffolding, portals, or simple CLI flows (whatever fits the org).
  • Documentation like it’s a feature: not an afterthought, not tribal knowledge.
  • Internal customer mindset: dev teams are users, and friction is a bug.

6) “How do we ship data products reliably?”

Data isn’t just infrastructure input any more - it’s a product. Platforms need to support:

  • Data pipeline orchestration: Airflow, Dagster, Prefect, Step Functions - whatever fits, provisioned as code.
  • Storage patterns with governance: data lakes (S3/GCS), warehouses (Snowflake, BigQuery), lakehouse (Delta, Iceberg).
  • Schema and catalog management: data contracts, schema registries (Glue, Unity Catalog), lineage tracking.
  • Data quality as a first-class concern: Great Expectations, dbt (data build tool) tests, Monte Carlo - automated, version-controlled, visible.
  • Secure, governed access: IAM for datasets, access logs, PII classification, compliance guardrails.
  • Infrastructure-as-code for data: Terraform modules for warehouses, DBT Cloud projects, Databricks workspaces, streaming infra (Kafka, Kinesis).

From a platform perspective: self-service data pipeline templates, standardized dbt projects, catalog-first workflows, quality checks baked into CI.

7) “How do we train, deploy, and monitor models?”

MLOps is platform engineering for machine learning:

  • Experiment tracking and model registry: MLflow, Weights & Biases, SageMaker Model Registry - reproducibility and versioning.
  • Training infrastructure: GPU/TPU provisioning (Kubernetes jobs, Kubeflow, managed services), spot instances for cost.
  • Feature stores: Feast, Tecton, SageMaker Feature Store - reusable feature pipelines, point-in-time correctness.
  • Model deployment patterns: batch inference, real-time endpoints, A/B testing, canary rollouts, shadow deployments for models.
  • Model observability: drift detection (data drift, concept drift), performance degradation, retraining triggers.
  • Reproducibility as a requirement: data versioning (DVC), containerized training, artifact lineage, “what model is running where.”
  • Compliance for ML: explainability, bias detection, audit trails, responsible AI guardrails.

What the platform provides: golden paths for “train → register → deploy → monitor,” self-service model deployment, automated retraining pipelines, observability dashboards for model performance alongside app metrics.

8) “How do we enable safe, cost-effective AI (LLMs, GenAI)?”

The newest frontier - infrastructure for generative AI and LLMs:

  • LLM serving infrastructure: vLLM, Text Generation Inference (TGI), Ray Serve, modal/replicate patterns, managed endpoints (Bedrock, Vertex AI).
  • Vector databases for RAG: Pinecone, Weaviate, pgvector, ChromaDB - retrieval-augmented generation infrastructure.
  • Prompt management and versioning: LangSmith, PromptLayer, prompts-as-code, evaluation frameworks.
  • Cost and rate limiting: GPU cost management, token usage tracking, quota enforcement, smart caching (semantic caching, KV caching).
  • Security for AI: prompt injection protection, PII scrubbing in requests/responses, model access control, compliance with AI regulations, and most importantly, treating everything as potentially hostile input.
  • Guardrails and evaluation: content filtering, toxicity checks, output validation, automated evaluation pipelines (RAGAS, LangChain evals).

Platform-wise, this means: self-service LLM endpoints with cost controls, RAG-as-a-service patterns, standardized evaluation frameworks, responsible AI checks baked into deployment, observability for token usage and latency.

The platform, end-to-end (a mental model)

                       +--------------------------------------------------+
                       |      Platform Engineering (paved road)           |
                       | (standards, automation, guardrails, golden paths)|
                       +--------------------------------------------------+

+-------------+                                    +--------------------+
| Developer/  |  push code/data/models             |    Git Repos       |
| Data Eng/   | ------------------------------>    | - app code         |
| ML Eng      |                                    | - IaC              |
+-------------+                                    | - data pipelines   |
                                                   | - ML training code |
                                                   | - prompts/configs  |
                                                   +--------------------+
                                                            |
                                                            | triggers
                                                            v
                                                   +-------------------+
                                                   |   CI Pipelines    |
                                                   | - app: test/build |
                                                   | - data: validate  |
                                                   | - ML: train/eval  |
                                                   +-------------------+
                                                            |
                                                            v
                                         +----------------------------------+
                                         |          Artifacts               |
                                         | - containers (app services)      |
                                         | - Python wheels, dbt packages    |
                                         | - trained models + metadata      |
                                         | - SBOM, signatures, lineage      |
                                         +----------------------------------+
                                                            |
                                                            | promote/deploy
                                                            v
                                         +----------------------------------+
                                         |        CD / Deployment           |
                                         | - GitOps (apps)                  |
                                         | - Airflow DAGs (data)            |
                                         | - Model serving endpoints (ML)   |
                                         +----------------------------------+
                                                            |
                                                            | rollout
                                                            v
       +----------------------+    +------------------------+     +--------------------+
       | App Runtime          |    |   Data Platform        |     | ML/AI Platform     |
       | - K8s/Cloud services |    | - Warehouses/Lakes     |     | - Model endpoints  |
       | - APIs, jobs         |    | - Pipelines (Airflow)  |     | - Feature stores   |
       +----------------------+    | - dbt transformations  |     | - Vector DBs (RAG) |
                |                  +------------------------+     | - LLM serving      |
                |                             |                   +--------------------+
                |                             |                             |
                +-----------------------------+-----------------------------+
                                              |
                                              v
                               +------------------------------+
                               |      Observability           |
                               | - logs, metrics, traces      |
                               | - data quality metrics       |
                               | - model drift, performance   |
                               | - cost (compute, tokens)     |
                               +------------------------------+
                                              |
                                              | alerts/SLOs
                                              v
                     +-------------------+          +--------------------+
                     | Security / Policy | <------  |  Feedback Loop     |
                     | - IAM/OIDC        |  informs | - incidents        |
                     | - secrets/KMS     |  priors  | - perf/cost        |
                     | - data governance |          | - data quality     |
                     | - AI guardrails   |          | - model drift      |
                     | - checks/controls |          | - dev/data/ML UX   |
                     +-------------------+          +--------------------+
                                                    |
                                                    | fixes/improvements
                                                    v
                                     +------------------------------+
                                     | Teams (Dev/Data/ML/AI Eng)   |
                                     +------------------------------+

Platform Engineering for Data, ML, and AI: Same Principles, New Artifacts

Platform engineering principles don’t change when you move from apps to data to ML to AI. What changes is what flows through the platform.

Domain Artifacts Deployment Target Observability Focus
Apps Containers, binaries, configs Kubernetes, serverless Logs, metrics, traces, SLOs
Data Datasets, schemas, dbt models Warehouses, lakes, pipelines Freshness, quality, lineage, cost
ML Trained models, feature pipelines Batch jobs, real-time endpoints Drift, accuracy, latency, retrain
AI (GenAI) LLM endpoints, prompts, vector indexes GPU clusters, managed APIs Token usage, cost, toxicity, evals

The platform playbook stays the same:

  • Golden paths: “Do it this way, and it just works.”
  • Self-service: Engineers provision what they need without waiting for tickets.
  • IaC everywhere: Terraform/OpenTofu modules for warehouses, GPU clusters, vector DBs, feature stores.
  • Security by default: IAM least privilege, encryption, compliance checks, PII handling, AI guardrails.
  • Observability built-in: Whether it’s API latency or data freshness or model drift - if it’s in prod, it’s monitored.
  • Feedback loops: Cost reports, quality metrics, developer friction - measure, improve, repeat.

In practice

Data platforms:

  • A data engineer runs terraform apply on a module that spins up a dbt Cloud project + Snowflake warehouse + Airflow DAG, all wired together.
  • Data quality tests (Great Expectations, dbt tests) run in CI before merging.
  • Schema changes are tracked in a catalog (Glue, Unity) with lineage from source → warehouse → BI dashboards.
  • Freshness and row count anomalies trigger alerts just like app SLOs.

ML platforms:

  • An ML engineer uses a “train-and-deploy” template: Docker + GPU Kubernetes job + model registry (MLflow) + endpoint deployment (SageMaker or KServe).
  • Feature store is provisioned as code; training uses versioned features with point-in-time correctness.
  • Model drift monitoring runs automatically; retraining pipelines trigger when drift exceeds thresholds.
  • Rollback is as simple as promoting a previous model version - just like rolling back a container.

AI infrastructure:

  • A team provisions an LLM endpoint (vLLM on GPU instances) + vector DB (Pinecone) + prompt versioning (LangSmith) via Terraform.
  • Prompts are version-controlled; evaluation pipelines (RAGAS) run on every PR.
  • Guardrails (toxicity filters, PII scrubbers) are enforced at the platform level before responses reach users.
  • Token usage and cost are tracked per team/project; quota limits prevent runaway bills.
  • RAG pipelines are standardized: ingest → embed → index → retrieve → generate, with observability at every step.

Why bother?

Whether you’re shipping containers, data pipelines, ML models, or LLM-powered features, the value proposition is the same:

  1. Speed: Teams move faster because the platform handles the undifferentiated heavy lifting.
  2. Safety: Guardrails (security, compliance, cost, quality) are baked in, not bolted on.
  3. Scale: One platform team enables dozens (or hundreds) of product/data/ML teams.
  4. Consistency: “It works on my machine” becomes “it works the same way everywhere.”
  5. Observability: You can’t improve what you don’t measure - platforms make measurement automatic.

And in 2026, that platform increasingly needs to support not just apps, but data products, ML models, and AI-powered features. Same principles, broader scope.

Wrapping up

Platform Engineering is building the roads so everyone else can drive fast and safe.

Whether that road carries Docker images to Kubernetes, dbt models to Snowflake, or trained models to SageMaker endpoints doesn’t change the core job: make the right way the easy way, automate everything, stay secure and observable, and treat internal users like customers.

If you’re doing DevOps, you’re already most of the way there. Platform engineering is just DevOps with a product mindset - and these days, that product increasingly includes data, ML, and AI infrastructure.

The tools change. The principles don’t.

Now go build some roads.