AI Product
AI Solutions Architect
Between a customer who wants "AI" and a cloud bill that won't bankrupt them stands the solutions architect. This is the person sketching whiteboards: which model, which vector store, which guardrails, which inference budget.
The role is partly engineering and partly translation — taking what a business is trying to achieve and shaping it into a reference architecture that other teams can build against. Strong architects know that token economics matter as much as accuracy, and that a slow answer is often a wrong answer.
Students grow into this path by getting their hands dirty with cloud services like Vertex AI or Bedrock and learning to defend architectural choices with numbers, not vibes.
- DesignIntermediateNew
Build an OWL Ontology for a Pharma R&D Knowledge Base
You receive a CSV-form starter knowledge base (around 4,000 compounds, 600 targets, 1,200 assays) and a list of 12 competency questions the scientists currently can't answer wit…
- Ontology Design
- Owl
- Knowledge Representation
Fuzzy Logic, Knowledge Representation, and Symbolic Reasoning - DesignSeniorNew
Multi-Region Failover for an Enterprise RAG Service
Design and prototype: (1) a primary-region deployment of the RAG service (vector DB + LLM inference + retrieval API), (2) a passive secondary region with replicated vector store…
- Multi Region Architecture
- Disaster Recovery
- Terraform
Cloud Computing for Data and ML - DesignBeginnerNew
Build a Multi-Criteria Vendor Selection Tool for an AI Consulting Firm
You will design a Multi-Criteria Decision Analysis (MCDA, a structured way to score options against weighted criteria) tool that accepts 6-10 vendor options, 6-12 weighted crite…
- Mcda
- Decision Support Systems
- Streamlit
Decision Support Systems and Decision Analysis - StrategyIntermediateNew
Design a PETs Strategy for an EU AI Act Use Case
Map the underwriting use case to applicable PETs across the data-lifecycle stages (training, evaluation, inference, monitoring). For each, document: privacy property gained, acc…
- Pets Strategy
- Differential Privacy
- Federated Learning
Privacy-Preserving Machine Learning 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
- DesignIntermediateNew
Build a Multi-Region Online Inference Service with SLAs
Design the topology: model artifact storage, regional inference fleets (Triton, vLLM, or BentoML), traffic router, observability stack (Prometheus + Grafana). Pick a rollout str…
- Inference Serving
- Multi Region Deployment
- Kubernetes Orchestration
Machine Learning Systems - StrategyIntermediateNew
Design a Post-Editing Workflow for a Cross-Border Fintech
You will design a 4-stage MTPE workflow: (1) source-content readiness check, (2) MT generation with the existing vendor, (3) post-editing with tier-based effort (light vs. full)…
- Mt Evaluation
- Workflow Design
- Neural Mt
Machine Translation - PresentationSeniorNew
Run a Post-Mortem on a Failed ML Deployment
You receive a packet: original training data sample, post-launch production logs, three Slack-style threads from the on-call rotation, and a summary of the telco's complaints. R…
- 5 Whys & Fishbone Root Cause Analysis
- Stakeholder Framing
- Model Monitoring
Machine Learning in Practice - ResearchSeniorNew
Multi-Tenant Vector Isolation for a B2B Knowledge Assistant
Build a small proof-of-concept in your chosen vector store (Pinecone or Qdrant — pick one and justify) that supports 10 simulated tenants with 1,000 vectors each. Implement the …
- Multi Tenant Isolation
- Vector Databases
- Threat Modeling
Vector Databases and Embeddings - 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.
- DesignIntermediateNew
Design a Lab-Automation Pipeline for a Bangalore Materials Startup
Design (not build) the full closed-loop lab pipeline: data layer (LIMS plus experiment store), model layer (a surrogate plus an acquisition function such as Expected Improvement…
- Systems Architecture
- Active Learning
- Mlops Design
AI for Science and Engineering - CodeIntermediateNew
Description-Logic Reasoner for Insurance-Policy Coverage Checks
You receive 50 representative coverage rules in plain English (from the current rule engine) and a sample of 1,000 anonymized claim cases with the current engine's outcomes (cov…
- Description Logics
- Owl
- Reasoning
Fuzzy Logic, Knowledge Representation, and Symbolic Reasoning - DesignBeginnerNew
Design a Retrieval Pipeline for a Climate-Research Open Archive
You receive a metadata sample (5,000 documents) plus 50 example researcher queries (mixed-language). Design a retrieval pipeline architecture that: (1) extracts and normalizes s…
- Retrieval Architecture
- Hybrid Search
- Multilingual Search
Information Retrieval and Search - PresentationIntermediateNew
Design a Hybrid Symbolic-Neural Agent for an Enterprise RAG Demo
Design a hybrid agent for a 'company-policy assistant' demo: a symbolic planner decomposes user goals into typed subtasks ('find policy', 'check applicability', 'compose answer'…
- Hybrid Ai
- Symbolic Planning
- RAG Architectures
Artificial Intelligence: Principles and Techniques 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
- DesignBeginnerNew
Design a Negotiation Support Tool for Climate-Tech Supplier Contracts
You will design and prototype a negotiation support tool for a single supplier contract with six issues (price per kg, delivery lead time, minimum order quantity, payment terms,…
- Negotiation Modeling
- Decision Support Systems
- Multi Issue Bargaining
Decision Support Systems and Decision Analysis - DesignIntermediateNew
Design a Customer 360 Graph for a Cross-Border Fintech
You receive 500 sample customer records across CRM, payments core, and KYC systems, plus a 50-record entity-resolution benchmark (pairs labelled same/different). Design an OWL o…
- Customer 360
- Entity Resolution
- Owl Ontology
Knowledge Graphs and Semantic Web - DesignIntermediateNew
Design Hybrid Search for an E-Commerce Product Catalog
You receive 80,000 anonymized product records (title, description, category, attributes) and a sample of 30,000 search log entries with click-through labels. Embed the catalog w…
- Hybrid Search
- Embedding Models
- Bm25
Vector Databases and Embeddings - AnalysisIntermediateNew
Benchmark NPUs for an Autonomous Forklift Vision Stack
You receive ONNX exports of the 3 production models, a labeled validation set of 2,000 forklift-camera frames, and developer-kit access to three NPU candidates (anonymized as NP…
- Edge Inference
- Npu Benchmarking
- Onnx Optimization
Edge ML and On-Device Machine Learning
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 Product
AI Product Designer
Designing for AI means designing for uncertainty. The interface has to invite the user to ask anything, but also signal honestly when the model is guessing, hallucinating, or refusing. AI product designers shape those moments — the empty state of a chat, the disclosure on a suggestion, the gentle correction when a response is wrong. Good work here looks like an experience that feels collaborative rather than oracular, where people leave with more agency than they came in with. Students grow into this role by treating accessibility and responsible-AI questions as design problems, not compliance checks. If you care about how language, trust, and visual rhythm meet on a screen, this is fertile ground.
AI Product
AI Product Manager
Shipping an AI feature is less like launching a button and more like releasing a new colleague into the company. AI product managers decide what that colleague is good at, where it shouldn't be trusted yet, and how to measure whether it's actually helping. The work blends classic product instincts — talking to users, sequencing roadmaps — with new muscles around evaluation metrics, annotation strategy, and the economics of inference. Strong PMs in this space write crisp definitions of done that include precision and recall alongside user outcomes. Students grow into this role by learning to read a model evaluation the way they'd read a usability test: with curiosity about what the numbers are hiding.
Industry teams behind a decade of practitioner briefs
Hiring from this pool?
Sponsor a challenge and meet candidates through actual work.
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 Redd Francisco on Unsplash.



















































































