Skills to Put on a Resume for an MLOps Engineer

The skills real MLOps Engineer postings screen for

Pulled from our MLOps Engineer role taxonomy — not a generic list. Each one names what a recruiter reads into it and, more usefully, how to actually back it up.

• Docker

  Tool

  Shows you can ship something that runs the same on your laptop as it does in production — a basic but non-negotiable expectation now.

  Evidence, not just a bullet: Link a repo with a Dockerfile you wrote and explain one non-obvious choice in it (a multi-stage build, a specific base image, a health check).

  Practice it:Containerize a Legacy Java Monolith and Move It to AWS ECSMigrating a 40-Person SaaS Scale-Up to Cloud-Native Architecture

• MLflow

  Tool

  MLflow is a specific tool real postings for this role name by name — listing it precisely (not folded into "familiar with standard tools") is what gets it past an ATS keyword match.

  Evidence, not just a bullet: Point to one real thing you built or produced with MLflow, not just the tool name on a line by itself.

  Practice it:Automate Retraining with a Drift-Triggered MLflow Pipeline

• Terraform

  Tool

  Infrastructure-as-code fluency — you can define and version cloud infrastructure instead of clicking through a console by hand.

  Evidence, not just a bullet: Link a repo with Terraform config you wrote and name one resource or module it provisions.

  Practice it:Containerize a Legacy Java Monolith and Move It to AWS ECSMulti-Region Active-Active Architecture for a Gaming Backend

• Python or JavaScript

  Listing a language only matters if you can point to something it built — recruiters skim past “Python” unless there's a repo or project attached.

  Evidence, not just a bullet: Link a GitHub repo with a script that solves a real problem — data cleaning, an API integration, a small app — with a README that explains what it does.

  Practice it:Design a Continuous Eval Pipeline for an Enterprise RAG ProductShip a Lightweight ML Microservice for an EdTech Reading App

• Airflow DAGs

  Airflow DAGs is one of the specific, verifiable skills recruiters screen for in this role — naming it plainly is what gets it past both an ATS keyword match and a human skim.

  Evidence, not just a bullet: Don't just list "Airflow DAGs" — attach one concrete example of using it: a project, a number, or an artifact a recruiter could actually look at.

  Practice it:Stand Up a Feature Store for a Series-B FintechAutomate Retraining with a Drift-Triggered MLflow Pipeline

• Kubernetes orchestration

  Tool

  One of the highest-demand infrastructure skills right now — proof you can run containerized workloads reliably at more-than-toy scale.

  Evidence, not just a bullet: Reference a specific manifest or Helm chart you wrote, and a real operational concern it handled (a liveness probe, an autoscaling rule, a rolling update).

  Practice it:Containerized Model Inference on Kubernetes for a FintechBuild a Canary Rollout for a Production Recommender

• AWS or Azure

  Cloud fluency recruiters filter on almost by keyword-match — but “used AWS” and “architected on AWS” read very differently.

  Evidence, not just a bullet: Name the specific services you’ve actually configured — e.g. set up an S3 lifecycle policy, wrote a Lambda, configured an IAM role — not just used passively.

  Practice it:Implement Authenticated Encryption for a Document ServiceContainerize a Legacy Java Monolith and Move It to AWS ECS

• A/B Testing

  Methodology

  A/B testing is proof you think in experiments, not opinions — you can design a test, pick a metric, and read a result without over-claiming significance.

  Evidence, not just a bullet: Describe one test end to end: the hypothesis, the metric that moved (or didn't), and what you'd do next.

  Practice it:Build a Canary Rollout for a Production Recommender

• Kubeflow pipelines

  Methodology

  Kubeflow pipelines is a named method, not a vague competency — claiming it says you can apply a specific, repeatable approach, not just "think analytically."

  Evidence, not just a bullet: Walk through one real case where you applied Kubeflow pipelines step by step, including what the output was.

• Version Control

  Methodology

  Version Control is a named method, not a vague competency — claiming it says you can apply a specific, repeatable approach, not just "think analytically."

  Evidence, not just a bullet: Walk through one real case where you applied Version Control step by step, including what the output was.

  Practice it:Set Up Trunk-Based Development on a 3-Branch Workflow

• CI/CD Pipelines

  Proof you think about how code gets to production safely and repeatedly, not just that it eventually works on your machine.

  Evidence, not just a bullet: Link a pipeline config (GitHub Actions, GitLab CI) you wrote and mention what it automated — tests, linting, a deploy gate.

Every challenge below is an AI-generated practice brief — not a real client engagement — that produces a submission you can point to as evidence for the skills above.

• 

  DesignIntermediateNew

  Design a Continuous Eval Pipeline for an Enterprise RAG Product

  Design (and partially build) a continuous-eval pipeline for a RAG system: (1) a structured eval set with at least 50 queries grouped by query class; (2) automated scoring (LLM-a…

  • Continuous Evaluation
  • LLM Evaluation
  • Retrieval Augmented Generation

  AI Measurement and Evaluation
• 

  CodeSeniorNew

  Build an MLOps Platform Slice for a Fintech Risk Team

  Across a 5-person team, ship (1) experiment tracking integrated into a sample model training job; (2) a model registry that promotes-by-tag; (3) a training pipeline orchestrated…

  • Mlops Design
  • Experiment Tracking
  • Model Registry

  Open coursework
• 

  CodeBeginnerNew

  Ship a Lightweight ML Microservice for an EdTech Reading App

  You receive 3 months of session telemetry (around 50M reading events, child-anonymized). Engineer features per session window, train a small classifier (logistic regression base…

  • Feature Engineering
  • Model Serving
  • Containerization

  Applied Machine Learning
• 

  CodeIntermediateNew

  Containerized Model Inference on Kubernetes for a Fintech

  You receive a pre-trained credit-risk model (a LightGBM model file) and a sample request payload. Containerize a FastAPI inference service, deploy to EKS or GKE (a single-zone c…

  • Kubernetes
  • Containerization
  • Autoscaling

  Cloud Computing for Data and ML
• 

  CodeIntermediateNew

  Quantize a CNN for Battery-Powered Wildlife Cameras at a Climate Nonprofit

  You receive an FP32 CNN (MobileNetV2 fine-tuned to 22 species, around 13 MB) and a hold-out test set of 4,000 images. Quantize to int8 (post-training quantization first, then qu…

  • Quantization
  • Qat
  • Edge Deployment

  Deep Learning
• 

  DesignSeniorNew

  Build an Edge MLOps Pipeline for a Smart-Agriculture Sensor

  You receive a fleet simulator (1,000 simulated sensors with bandwidth + battery profiles), a model registry stub, and the current firmware's model-loading interface. Design and …

  • Edge Mlops
  • Ota Updates
  • Model Versioning

  Open coursework