Python
Use a project where Python changes a real implementation decision. Show the code, artifact, test, metric or operational evidence behind the claim.
CAREER PATH · AI Engineer
Move beyond a chat wrapper and prove evaluation discipline.
Show retrieval, data handling, evaluation, observability and cost/latency tradeoffs around AI systems.
PythonLLMsRetrievalEvaluationData pipelinesObservability
WHAT TO PROVE
Turn role requirements into visible evidence.
A portfolio is stronger when each project makes one or two important capabilities easy to inspect instead of trying to mention every technology at once.
LLMs
Use a project where LLMs changes a real implementation decision. Show the code, artifact, test, metric or operational evidence behind the claim.
Retrieval
Use a project where Retrieval changes a real implementation decision. Show the code, artifact, test, metric or operational evidence behind the claim.
Evaluation
Use a project where Evaluation changes a real implementation decision. Show the code, artifact, test, metric or operational evidence behind the claim.
Data pipelines
Use a project where Data pipelines changes a real implementation decision. Show the code, artifact, test, metric or operational evidence behind the claim.
Observability
Use a project where Observability changes a real implementation decision. Show the code, artifact, test, metric or operational evidence behind the claim.
RECOMMENDED PROJECTS
6 projects for the AI Engineer path.
Choose one project you can finish, deploy and explain deeply. Quality of proof matters more than collecting unfinished repositories.
Advanced2–3 weekends
RAG Evaluation Workbench
Build retrieval-augmented generation with repeatable quality evaluation instead of stopping at chat.
LLMsRetrievalEvaluationData pipelinesObservability
Open project brief →Intermediate2 weekends
Support Ticket Triage Assistant
Classify and route support tickets with confidence thresholds, human review and evaluation.
ClassificationLLMsEvaluationHuman-in-loopAPIs
Open project brief →Advanced2 weekends
Document Extraction Pipeline
Extract structured fields from messy documents and evaluate correctness against labeled examples.
OCR/visionStructured outputEvaluationPipelinesValidation
Open project brief →Intermediate1–2 weekends
Semantic Search Engine
Index a real corpus, implement semantic retrieval and compare it with keyword baselines.
EmbeddingsVector searchEvaluationAPIsRanking
Open project brief →Advanced3 weekends
AI Agent Operations Console
Build a tool-using agent with permissions, traces, retries and explicit human approval points.
AgentsTool useSafetyTracingEvaluation
Open project brief →Intermediate1 weekend
Prompt Regression Test Suite
Create versioned prompts, test cases and automated quality/cost regression checks.
Prompt engineeringEvaluationCIMetricsLLM APIs
Open project brief →RELEVANT OFFICIAL CERTIFICATIONS
Credentials that can complement the project proof.
These are optional signals from official issuers, not requirements for the role. When a pasted job explicitly mentions a credential, SkillChirp can surface it separately from project skill coverage.
MicrosoftFoundational
Microsoft Certified: Azure AI Fundamentals
Beginner AI credential covering AI concepts and implementing AI solutions with Microsoft Azure tooling.
AzureAIMachine learningGenerative AIPython
Google CloudProfessional
Google Cloud Professional Machine Learning Engineer
Professional ML credential for building, productionizing and monitoring machine-learning and generative-AI solutions.
Google CloudMachine learningMLOpsGenerative AIMonitoring
HOW TO USE THESE PROJECTS
Build, document, then make the proof easy to review.
Pick one missing signal
Choose the project that best fills a gap in your current portfolio rather than the one with the longest technology list.
Ship a working core
Keep scope small enough to finish. A deployed, testable core gives you more evidence than an ambitious unfinished system.
Document the tradeoffs
Explain architecture, constraints, failure states, testing and what you would change with more time.
Validate the work
Use SkillChirp discovery and Battles where appropriate to expose the project to outside feedback instead of leaving it hidden in a repository.
Make the project visible.
After you ship it, use the rest of SkillChirp to get discovery, validation and feedback instead of leaving the work hidden in a repository.