Skills to Put on a Resume for an AI Engineer

The skills real AI Engineer postings screen for

Pulled from our AI 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.

• Model Evaluation

  Methodology

  Model Evaluation 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 Model Evaluation step by step, including what the output was.

  Practice it:Cost-Model a Foundation-Model API MigrationBuild a Face-Anonymization Tool for a Civic-Tech Newsroom

• RAG architectures

  Methodology

  RAG architectures 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 RAG architectures step by step, including what the output was.

  Practice it:Multi-Agent Research Assistant for Biotech Patent ReviewShip an MVP RAG Knowledge Assistant for a Climate-Tech Startup

• 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:Implement Model Predictive Control for a Delivery RobotLocalize a Mobile Robot with Particle-Filter SLAM

• PyTorch or TensorFlow

  Naming a specific deep-learning framework — not just “machine learning” — says you’ve actually built and trained a model, not just called an API.

  Evidence, not just a bullet: Link a repo with a model you trained, even on a small dataset, and mention the architecture and one hyperparameter choice you made.

  Practice it:Finetune a Diffusion Model for Sustainable-Fashion MockupsEnd-to-End Lane Following on a Donkeycar Platform

• Distributed training

  Technical

  Says you can train models across multiple GPUs or machines, not just on a single laptop — a scale problem distinct from the modeling itself.

  Evidence, not just a bullet: Reference a training run you scaled across devices and one bottleneck you had to work around (data loading, gradient sync).

  Practice it:Design a Distributed Training Job for a 13B-Parameter ModelAuto-Tune a Distributed Training Cluster's Throughput

• 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.

• 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

• Fine-tuning

  Technical

  Shows you can adapt an existing pretrained model to a specific task instead of training from scratch — the practical, resource-efficient skill most real ML work actually uses.

  Evidence, not just a bullet: Reference a model you fine-tuned, the base model you started from, and what changed in performance.

  Practice it:QLoRA Fine-Tune for a Customer-Support Domain Assistant

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.

• 

  CodeIntermediateNew

  Finetune a Diffusion Model for Sustainable-Fashion Mockups

  You receive 1,200 product photos with paired captions and the brand's style guide. Fine-tune a Stable-Diffusion-class base model with LoRA (Low-Rank Adaptation, a parameter-effi…

  • Diffusion Models
  • Lora Finetuning
  • Pytorch

  Open coursework
• 

  CodeIntermediateNew

  Implement Model Predictive Control for a Delivery Robot

  You receive a kinematic bicycle model of the robot, a published track layout, and 30 minutes of recorded waypoint trajectories. Implement a nonlinear MPC controller using acados…

  • Model Predictive Control
  • Optimal Control
  • Robotics Simulation

  Open coursework
• 

  CodeIntermediateNew

  Localize a Mobile Robot with Particle-Filter SLAM

  You receive 4 ROS bags from real customer plants, each containing 2D LiDAR scans, wheel odometry, and ground-truth poses (from a motion-capture cell used only for evaluation). I…

  • State Estimation
  • Particle Filter
  • Slam

  Advanced Robotics
• 

  CodeSeniorNew

  Plan Under Uncertainty for a Warehouse Restocking Robot

  You receive a discrete-event simulator of a 1,200-shelf warehouse with calibrated optical-scanning error rates and stock-out cost per shelf. Formulate the restocking decision as…

  • Planning Under Uncertainty
  • Pomdp
  • Monte Carlo Planning

  Advanced Robotics
• 

  CodeIntermediateNew

  Build an Internal-Tools Agent for a Mid-Cap Enterprise

  You receive OpenAPI specs for 4 mock internal APIs and 30 reference question-answer pairs spanning easy lookups and multi-tool chains. Build the agent using an LLM tool-use fram…

  • LLM Agents
  • Tool Use
  • Agent Evaluation

  AI Agents and LLM-Based Agents
• 

  CodeIntermediateNew

  Multi-Agent Research Assistant for Biotech Patent Review

  You receive 20 historical patent applications with the firm's own prior-art memos as ground truth. Design and build a 3-agent system: (a) Searcher — issues queries to a patent-s…

  • LLM Agents
  • Multi Agent Collaboration
  • Agent Evaluation

  AI Agents and LLM-Based Agents