AI Engineering
AI Engineer
Between a promising research paper and a feature people actually use sits a long, unglamorous bridge — and AI engineers build it. The job is taking models that work in notebooks and shaping them into systems that hold up under real traffic, real costs, and real users with messy questions.
Good work here looks like a retrieval pipeline that gets answers right ninety-something percent of the time, with evaluation harnesses catching regressions before they ship. Students grow into this role by treating Python and PyTorch as instruments rather than checkboxes, then learning how to reason about latency, evaluation, and cost together.
If you enjoy stitching ideas into running software, this path will feel like home.
- CodeIntermediateNew
Agentic RAG with Context-Window Budgeting
You receive a synthetic dataset of 60 founder-style queries paired with 'workspaces' (each up to 500 documents across 3 source types), plus gold-standard answers and citation li…
- Agentic RAG
- Context Window Management
- Iterative Retrieval
Retrieval-Augmented Generation - AnalysisBeginnerNew
Cost-Model a Foundation-Model API Migration
You receive: 90 days of API logs (request volume, token distributions), the customer's golden eval set of 200 prompts, the incumbent and new pricing schedules, and quality ratin…
- Cost Modeling
- Ai Workforce Strategy
- Model Evaluation
AI for Business and AI Product Management - CodeBeginnerNew
Reason about Drone Mission Plans with Probabilistic Logic
Build a small Bayesian network (around 12 nodes) capturing weather, no-fly-zone proximity, battery state, operator certification, and mission risk. Implement exact inference (va…
- Bayesian Networks
- Probabilistic Inference
- Knowledge Representation
Introduction to Artificial Intelligence - CodeIntermediateNew
Design Safe Navigation Behavior for a Hospital Delivery Robot
You receive a dataset of 200 anonymized hospital corridor traces (people positions over time from the robot's LIDAR) plus the current planner's parameters. Design a policy that …
- Human Aware Navigation
- Ros2
- Motion Planning
Human-Robot Interaction Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- 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 - CodeFoundationalNew
Build a Best-First Search Solver for a Delivery Startup's Test Lab
Build a Python module exposing uniform-cost search and A-star search on a 2D grid with weighted cells (open road, traffic, no-go). Implement two admissible heuristics for A-star…
- Search Algorithms
- Heuristic Design
- Python Or Javascript
Introduction to Artificial Intelligence - CodeIntermediateNew
Reason over a Climate Policy Knowledge Graph for an EU Think Tank
Design a knowledge graph schema covering regulations, member states, sectors, transposition dates, and source-document citations. Ingest a curated dataset of around 200 nodes th…
- Knowledge Graphs
- Knowledge Representation
- Rule Based Reasoning
Artificial Intelligence: Principles and Techniques - CodeIntermediateNew
ReAct Agent for Legal-Research Tool-Use
You receive 30 research questions with paralegal-written gold answers and citation lists, plus stubbed implementations of the 4 tools (you do not need to build retrieval — just …
- React Prompting
- Tool Use
- Agent Design
Prompt Engineering - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- CodeIntermediateNew
Build a Tool-Calling Agent for an Internal Reporting Bot
You will implement the agent in either LangChain or LlamaIndex (your choice; defend it in the readme). Wire 4 tools: (1) read-only SQL on a sample warehouse, (2) a mocked BI met…
- Agent Orchestration
- Tool Calling
- Langchain Or Llamaindex
LLM Application Development - CodeIntermediateNew
Build a Cross-Lingual Retrieval-Augmented QA System
Index around 5,000 internal-knowledge docs across the three languages using a multilingual embedding model (e.g., multilingual-e5 or BGE-M3). Build the retrieval-then-answer pip…
- RAG Architectures
- Cross Lingual Retrieval
- Multilingual Embeddings
Neural Networks for NLP - CodeBeginnerNew
End-to-End Lane Following on a Donkeycar Platform
Use the public Donkeycar Tub dataset (or collect about 30 minutes of driving on the simulator). Train a CNN-policy baseline (the Donkeycar default architecture is fine) that pre…
- End To End Learning
- Imitation Learning
- Pytorch Or Tensorflow
AI for Autonomous Vehicles - CodeIntermediateNew
Plan Warehouse Pick Routes with a Classical Planner
You receive a stylized warehouse map (aisle graph), 30 sample shifts of pick tasks, and the current heuristic's outputs. Write a PDDL domain + problem generator, solve with at l…
- Pddl Modeling
- State Space Search
- Classical Planning
Automated Planning Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- CodeBeginnerNew
Build a Math Intelligent-Tutoring Assistant for High Schoolers
You receive: a curated set of 40 algebra problems with worked solutions, the company's pedagogy rubric ('hint, don't reveal' principle), and a baseline 'just answer' chatbot for…
- Intelligent Tutoring
- Prompt Patterns
- Ai Agents
AI in Education and Learning Analytics - CodeSeniorNew
Grounded Language for a Robotics Pick-and-Place Demo
Use a tabletop simulator (PyBullet or Isaac Sim, both open) with 5 object types and 5 spatial relations (left of, right of, behind, in front of, on top of). Curate or generate a…
- Grounded Language Understanding
- Semantic Parsing
- Perception
Computational Semantics - AnalysisBeginnerNew
Chunking Strategy Bake-Off for Financial Filings
You receive 40 anonymized 10-K filings and 100 labeled questions split into 50 narrative (e.g., 'what is the company's main risk factor?') and 50 numerical (e.g., 'what was oper…
- Document Chunking
- Semantic Chunking
- Layout Aware Chunking
Retrieval-Augmented Generation - CodeIntermediateNew
Hierarchical Plans for an Aerospace Maintenance Crew Scheduler
You receive a synthetic week of 80 work orders with hierarchical decompositions, technician certifications, and shared-tool constraints. Implement an HTN planner (PyHOP or HDDL …
- Htn Planning
- Domain Modeling
- Constraint Handling
Automated Planning - CodeIntermediateNew
Build a Multimodal Generation Pipeline for a Tourism Operator
You receive 40 sample 30-second videos shot by tour guides, the operator's brand voice doc, and SEO keyword lists for EN/PT/ES. Build a pipeline that (1) extracts a representati…
- Multimodal Generation
- Vision Language Models
- LLM Inference
Generative AI - CodeBeginnerNew
Plan Safe Paths for a Last-Mile Sidewalk Robot
You receive 4 hours of recorded sidewalk traversals with annotated pedestrian tracks, occupancy grids, and a map of the pilot neighborhood. Implement a sampling-based planner (R…
- Motion Planning
- Sampling Based Planning
- Cost Function Design
Robot Perception and Autonomy - 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
Advanced Robotics - CodeBeginnerNew
Build a Hybrid Search for an Enterprise RAG Knowledge Base
You receive 50,000 internal documents (anonymized policy memos, regulation excerpts, internal FAQs) plus a 300-query benchmark with binary relevance labels for the top-10 return…
- Hybrid Search
- Bm25
- Dense Retrieval
Information Retrieval and Search - CodeBeginnerNew
Build Semantic Search for an Internal Engineering Wiki
You receive a Confluence XML export (~12k pages, ~80 MB of text) and a hand-labeled benchmark of 50 internal queries with ground-truth doc IDs. Chunk and embed the corpus with a…
- Embedding Models
- Vector Database Basics
- Pgvector
Vector Databases and Embeddings - CodeIntermediateNew
Build a LangGraph Multi-Agent Researcher
Design the four-agent topology with explicit message contracts. Implement each agent as a separate LLM call with role-specific system prompts, tool access (web search for resear…
- Multi Agent Orchestration
- Langgraph Or Crewai Workflows
- Tool Use
Multi-Agent Systems - CodeIntermediateNew
Design a Force-Controlled Polishing Skill for a Watchmaker
You receive simulated polishing trajectories from the manufacturer's robot, force-sensor logs from 20 master-craftsman demonstrations, and a quality-rubric (mirror finish 1-5) f…
- Impedance Control
- Force Control
- Manipulation
Robotics - CodeIntermediateNew
Ship a Streaming RAG Endpoint with Caching and Fallbacks
You will build a FastAPI service exposing one POST /chat endpoint that (1) streams tokens via Server-Sent Events, (2) caches identical (system_prompt, query, retrieved_context) …
- LLM API Integration
- Streaming
- Response Caching
LLM Application Development - CodeBeginnerNew
Fuzzy-Logic Controller for a Sustainable-Greenhouse Operator
You receive a year of 15-minute climate logs (inside/outside temperature, humidity, light, CO2), the current rule-based controller, and the head grower's qualitative description…
- Fuzzy Logic
- Mamdani Inference
- Rule Based Systems
Fuzzy Logic, Knowledge Representation, and Symbolic Reasoning - 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…
- Ai Agents
- Tool Use
- Agent Evaluation
AI Agents and LLM-Based Agents - DesignFoundationalNew
Build a Tic-Tac-Toe-Style Game Agent for an Edtech Demo
Implement Connect-Four (7-column, 6-row board) in Python plus a minimax agent with alpha-beta pruning, configurable search depth, and a simple heuristic evaluation function for …
- Game Playing Ai
- Minimax
- Alpha Beta Pruning
Introduction to Artificial Intelligence - CodeIntermediateNew
Design an SAT-Based Verifier for an Autonomous-Vehicle Test Lab
Model a simplified four-way intersection: agent positions, lights, and discrete time steps. Define 5 safety properties in propositional logic (e.g., 'no two agents in the inters…
- Sat Solving
- Logical Inference
- Formal Verification
Artificial Intelligence: Principles and Techniques - 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…
- Ai Agents
- Multi Agent Collaboration
- Agent Evaluation
AI Agents and LLM-Based Agents - 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 - CodeBeginnerNew
Hybrid Search RAG for a HR-Policy Assistant
You receive 1,800 pages of policy documents (Markdown) and 150 labeled question-answer pairs with the gold source policy IDs. Build a hybrid retrieval pipeline: BM25 + dense emb…
- Hybrid Search
- Bm25
- Dense Retrieval
Retrieval-Augmented Generation - CodeBeginnerNew
Build a Video-Question-Answering Demo on a Budget
Pick the model (Video-LLaVA, VideoChat2, or LLaVA-Video) and justify on the A10G budget. Build a Streamlit demo: upload video, ask question, get answer with cited frame timestam…
- Video Language Models
- Multimodal Fusion
- Streamlit
Multimodal Machine Learning - AnalysisIntermediateNew
Cut Latency and Cost on a High-Volume Summarization Service
You receive 30 days of anonymized request logs (prompt token counts, completion token counts, latencies, models used). Profile the cost and latency distribution, then design and…
- Finops & Cost Optimization
- Latency Optimization
- Prompt Compression
LLM Application Development - CodeIntermediateNew
Build a Vector-Search Backend for an Enterprise AI Knowledge Assistant
You receive a corpus of around 20,000 PDFs (mixed scanned and digital) totalling around 30 GB and a labeled retrieval set of 200 queries with human-judged ground-truth passages.…
- RAG Architectures
- Vector Database Basics
- Word Embeddings
Data Engineering and Big Data Systems - CodeBeginnerNew
Implement a Constraint Solver for a Lisbon Tourism Scheduler
Model the next-week schedule as a CSP: variables are (guide, day, slot) assignments; domains are available routes; constraints encode language requirements, max consecutive tour…
- Constraint Satisfaction
- Backtracking Search
- Python Or Javascript
Introduction to Artificial Intelligence - CodeBeginnerNew
Prototype a Multimodal Visual-Question-Answering Demo
You will use a small open-source vision-language model (e.g., LLaVA-1.5-7B or PaliGemma) and prompt-engineer it for the warehouse-VQA task. Build a Gradio web demo. Construct a …
- Vision Language Models
- Multimodal Perception
- Prompt Patterns
Machine Perception - AnalysisIntermediateNew
Evaluate an Agent Suite on the SWE-Bench-Style Coding Benchmark
You receive a sandboxed set of 50 small repo-modification tasks (test-passing as the success signal). Run 3 open-source agent frameworks (e.g., OpenHands, SWE-agent, and Aider) …
- Ai Agents
- Agent Evaluation
- Benchmarking
AI Agents and LLM-Based Agents - CodeFoundationalNew
Tune a Pick-and-Place Controller for a Cosmetics Co-Packer
You receive 4 hours of logged trajectories from the existing controller (joint positions, target poses, miss/success labels) and read/write access to the controller config (YAML…
- Motion Control
- Trajectory Tuning
- Robot Kinematics
Robotics - CodeIntermediateNew
Ship an MVP RAG Knowledge Assistant for a Climate-Tech Startup
As a 4-person team across a 6-week sprint, ship: (1) an ingestion pipeline for around 4,000 mixed PDFs and markdown files; (2) a vector store with documented chunking strategy; …
- RAG Architectures
- Software Engineering For Ai
- Vector Databases
AI Software Engineering Group Project - CodeIntermediateNew
Wire a Knowledge Graph into a Pharma RAG Assistant
You receive: 100 internal benchmark questions with reference answers; a 50,000-document anonymized RAG index; a curated drug-target-disease KG (~80,000 triples) loaded into a tr…
- Kg Grounded RAG
- Sparql
- Entity Linking
Knowledge Graphs and Semantic Web - CodeBeginnerNew
Build a Face-Anonymization Tool for a Civic-Tech Newsroom
Use a pretrained face detector (RetinaFace or YOLOv8-face is fine). Build a Python tool with a Gradio or Streamlit UI that: (1) detects faces in an uploaded photo, (2) shows det…
- Object Detection
- Image Processing
- Opencv
Computer Vision (Undergraduate) - CodeIntermediateNew
Temporal Planner for a Robotics Mission Operator
You receive 30 days of mission logs with task lists, time windows, and actual durations. Encode the planning problem with temporal PDDL (PDDL 2.1 durative actions) and solve wit…
- Temporal Planning
- Pddl Modeling
- Simulation
Automated Planning - DesignIntermediateNew
Visualize Embedding Drift for a RAG Knowledge Assistant
You receive weekly snapshots over 12 weeks of around 50,000 document embeddings each (1024-dim). Design and build a visualization tool that: (a) projects each snapshot to 2D wit…
- Word Embeddings
- Dimensionality Reduction
- Umap
Data Visualization - ResearchIntermediateNew
Neuro-Symbolic Question Answering on an Enterprise Knowledge Graph
You receive a curated Turtle-format knowledge graph (around 2 million triples covering organizational structure, products, projects), 200 labeled question-SPARQL pairs split 140…
- Neuro Symbolic
- Sparql
- Knowledge Graphs
Fuzzy Logic, Knowledge Representation, and Symbolic Reasoning - 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 Or Tensorflow
Advanced Deep Learning - ResearchIntermediateNew
QLoRA Fine-Tune for a Customer-Support Domain Assistant
You receive 8,000 anonymized support ticket pairs (question -> agent response), the company's product documentation (around 600 pages), and a strong RAG baseline already running…
- Qlora
- Fine Tuning
- RAG Architectures
Fine-Tuning Large Language Models
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Related roles you may want to explore
Browse all roles →AI Engineering
Computer Vision Engineer
Teaching a machine to see is harder than it sounds and more interesting than it looks. Computer vision engineers shape the systems that read documents, navigate self-driving cars, screen medical images, and answer questions about photos. The role mixes the math of multi-view geometry with the engineering grind of getting models small and fast enough to run where they're needed — sometimes on a phone, sometimes on a robot. Good work here looks like a pipeline that holds up in real lighting, real motion, and real failure modes. Students grow into this path by getting hands-on with OpenCV and PyTorch early, then learning the harder craft of optimizing models without quietly destroying their accuracy.
AI Engineering
Machine Learning Engineer
A model that works on a laptop and a model that works for millions of users are two very different artifacts, and machine learning engineers live in the gap between them. The role exists to take research-grade ML and turn it into reliable production systems, which means caring about latency, retraining pipelines, and what happens when the data distribution shifts at three in the morning. Students grow into this through hands-on work with PyTorch or TensorFlow plus enough software engineering discipline to run real CI/CD. Tools like AWS SageMaker become part of the workflow. Strong ML engineers can talk shop with data scientists on one side and platform engineers on the other, and that bilingual quality is often what gets them hired.
AI Engineering
MLOps Engineer
Models in production fail in stranger ways than models in notebooks ever could. The MLOps engineer is the person who anticipates those failures and builds the scaffolding that makes machine learning survive contact with real users. Think feature stores that stay consistent between training and serving, deployment pipelines through MLflow that make rollbacks boring, and observability that catches drift before stakeholders notice. The work sits at the intersection of platform engineering and data science, and rewards people who like building tools other engineers will rely on. A student grows into this role by getting comfortable with Kubernetes early and developing taste for what a healthy ML system actually looks like under load.
AI Engineering
NLP Engineer
Language is messy. People misspell, contradict themselves, ask the same thing five different ways, and expect a machine to understand. NLP engineers build the systems that try. The role spans classical text processing in spaCy, modern retrieval-augmented architectures stitched together with LangChain, and the constant judgment calls about when to fine-tune, when to prompt, and when to fall back to rules. It rewards people who love both linguistics and systems thinking. Students grow into it through small projects — a question-answering bot over their notes, a classifier for their inbox — that surface the real failure modes of language models. Good NLP engineers obsess over evaluation as much as architecture.
AI Engineering
Prompt Engineer
Writing instructions for a model is a strange new craft. The words you choose, their order, the examples you include — all shape what a multi-billion-parameter system actually does next. Prompt engineers treat this as a real engineering discipline: versioning prompts in tools like PromptLayer, running evaluations across thousands of test cases, optimizing for cost and latency in production, and collaborating with domain experts to encode their judgment in text. The role is new enough that students often help define it on the job. Growing into it means building intuition for how models fail, when to fine-tune instead, and how to write specs precise enough to ship. Good prompt engineers measure everything and trust vibes only as a starting point.
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Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.
Skills and disciplines shown on this page are derived from the Ewance challenge catalogue. When the median annual salary is available for this role via Adzuna, it will be shown above with the sample size and country.
Portrait: Photo by Ludovic Migneault on Unsplash.