Skills to Put on a Resume for an NLP Engineer

The skills real NLP Engineer postings screen for

Pulled from our NLP 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.

• 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 ReviewBuild the PRD for an Internal RAG Knowledge Assistant

• Prompt Patterns

  Prompt Patterns 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 "Prompt Patterns" — attach one concrete example of using it: a project, a number, or an artifact a recruiter could actually look at.

  Practice it:Finetune a Diffusion Model for Sustainable-Fashion MockupsBuild an Internal-Tools Agent for a Mid-Cap Enterprise

• 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:Semantic Parser for an Enterprise Analytics AssistantDistributional Embeddings for a Multilingual Legal Search

• 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:Semantic Parser for an Enterprise Analytics AssistantFine-Tune a Transformer for Customer-Support Triage at an Enterprise AI Vendor

• Attention mechanisms

  Methodology

  Attention mechanisms 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 Attention mechanisms step by step, including what the output was.

  Practice it:Build an Attention-Visualization Tool for Translation Quality Audit

• Hugging Face Transformers

  Tool

  Hugging Face Transformers 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 Hugging Face Transformers, not just the tool name on a line by itself.

  Practice it:Fine-Tune a Transformer for Customer-Support Triage at an Enterprise AI VendorInstruction-Tune a Small Model for an Edtech Tutor

• 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

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

  Semantic Parser for an Enterprise Analytics Assistant

  Define a small typed query language (filter, aggregate, group_by, time_range, metric). Curate or write 200 training examples covering the controlled subset and 50 held-out test …

  • Semantic Parsing
  • Grammar Design
  • Transformer Models

  Computational Semantics
• 

  CodeIntermediateNew

  Distributional Embeddings for a Multilingual Legal Search

  Use a public multilingual corpus (e.g., MultiEURLEX or a subset of EUR-Lex) plus a small hand-built test set of around 100 cross-lingual query-passage pairs. Fine-tune (or evalu…

  • Distributional Semantics
  • Multilingual NLP
  • Sentence Embeddings

  Computational Semantics
• 

  CodeIntermediateNew

  Natural Language Inference for an HR-AI Compliance Tool

  Use SNLI/MNLI/ANLI as starting data and curate 200 domain-specific HR examples (synthetic or anonymized) for fine-tuning. Fine-tune a small encoder (DeBERTa-v3-base or similar),…

  • Natural Language Inference
  • Transformer Models
  • Fine Tuning

  Computational Semantics
• 

  CodeIntermediateNew

  Lambda-Calculus Semantic Parser for a Math-Tutor EdTech

  Define a small typed lambda-calculus representation for linear equations and a small set of word-problem templates (rate, age, mixture). Build a parser that maps surface express…

  • Semantic Parsing
  • Lambda Calculus
  • Symbolic Reasoning

  Computational Semantics
• 

  CodeIntermediateNew

  Fine-Tune a Transformer for Customer-Support Triage at an Enterprise AI Vendor

  You receive 240,000 labeled support tickets across 14 queues, with English, Bahasa Indonesia, and Tagalog. Fine-tune a multilingual transformer encoder (XLM-RoBERTa-base is a st…

  • Transformers
  • Fine Tuning
  • Multilingual NLP

  Deep Learning
• 

  CodeIntermediateNew

  Instruction-Tune a Small Model for an Edtech Tutor

  You receive a 1.5B base model (e.g., SmolLM-1.7B or Qwen-1.8B), permission to use 2 hours of a rented A100, and a curated seed of around 5,000 math-tutoring dialogues. Augment w…

  • Instruction Tuning
  • Supervised Fine Tuning
  • Dataset Curation

  Fine-Tuning Large Language Models