Retrieval quality, access boundaries and grounded answers. Compare a fresher portfolio with early-career contributions, production ownership and senior technical influence. Choose the example that fits your actual experience.
Fictional samples — replace with your own experience.
Fictional educational example. Names, employers, education and project claims are illustrative, not a real candidate or completed AIJobFever implementation. Replace them with your own truthful evidence before applying. The example timelines illustrate progression, not required graduation years or a rule that senior candidates must become managers.
CHOOSE YOUR EXPERIENCE
Four stages. Different evidence.
Use the stage that matches the responsibility you can demonstrate. The year ranges are guidance, not a requirement to claim a particular title. The 5+ example follows a senior individual-contributor path, not mandatory people management.
FRESHER · FICTIONAL SAMPLE
RAG Engineer: Fresher
Education, portfolio projects and foundational skills; no invented employment.
Bengaluru, India · alex@example.com · example.com/portfolio
Professional summary
Entry-level RAG engineer with portfolio projects in permission-aware search and retrieval diagnosis. Uses labelled passage relevance and synthetic access rules to examine retrieval failures before judging answer fluency; has no claimed paid RAG experience.
Built a retriever over synthetic team documents and filtered candidates by permitted access before generating answers.
Added tests for cross-team leakage, deleted documents and queries with no supporting passage.
Retrieval diagnosis notebook
Compared lexical and embedding retrieval against hand-labelled relevant passages.
Examined chunk boundaries and ranking errors separately from answer-generation errors.
Education
Bachelor's degree in a relevant discipline — replace with your qualification
Example Institute (fictional) · 2022–2026
Replace with your actual institution, dates, coursework and qualification. Remove anything you cannot substantiate.
SHOW YOUR CONTRIBUTION
A stronger Fresher bullet
TOO VAGUE
Expert in all RAG frameworks.
MORE USEFUL EVIDENCE
Built a permission-aware RAG prototype and compared lexical versus embedding retrieval using labelled passage-level relevance.
Use this structure only for work you actually did. Add measured results only when you can explain the baseline, evaluation method and workload. The fictional examples do not supply fabricated performance percentages for your application.
1–2 YEARS · FICTIONAL SAMPLE
RAG Engineer: 1–2 years
Specific team contributions, supervised releases and practical troubleshooting.
Bengaluru, India · alex@example.com · example.com/portfolio
Professional summary
RAG engineer contributing ingestion checks and search evaluation within an existing knowledge system. Reproduces missing-document and ranking failures and keeps access-filter testing separate from answer-generation testing.
Example Technology Studio (fictional) · Bengaluru, India · 2025–2026
Added parser checks for empty pages and duplicate document identifiers before indexing.
Labelled relevant passages for a reviewed query set and investigated cases where the correct document was not retrieved.
Implemented fixtures for team-based metadata filters under the existing authorization design.
AI projects
Ingestion rejection diagnostics
Recorded parser failures with source identifiers and retryable versus non-retryable reasons.
Replayed a redacted fixture set to verify that failed documents did not silently vanish.
Passage relevance contribution
Compared lexical and embedding candidates for a bounded set of support queries.
Documented disagreement cases for review instead of declaring one retriever universally better.
Education
Bachelor's degree in a relevant discipline — replace with your qualification
Example Institute (fictional) · 2021–2025
Replace with your actual institution, dates, coursework and qualification. Remove anything you cannot substantiate.
SHOW YOUR CONTRIBUTION
A stronger 1–2 years bullet
TOO VAGUE
Built enterprise RAG using a vector database.
MORE USEFUL EVIDENCE
Added ingestion rejection diagnostics and labelled-passage fixtures to investigate missing-document and ranking failures.
Use this structure only for work you actually did. Add measured results only when you can explain the baseline, evaluation method and workload. The fictional examples do not supply fabricated performance percentages for your application.
2–5 YEARS · FICTIONAL SAMPLE
RAG Engineer: 2–5 years
End-to-end ownership, production evaluation and cross-team delivery.
Bengaluru, India · alex@example.com · example.com/portfolio
Professional summary
RAG engineer owning a permission-aware knowledge workflow across ingestion, hybrid retrieval and grounded answers. Evaluates relevance, freshness and answer support separately, with clear recovery paths for deletion, reindexing and incomplete evidence.
Example Technology Studio (fictional) · Bengaluru, India · 2022–2026
Owned hybrid retrieval and reranking with a versioned query set and reviewed passage-level relevance labels.
Implemented source-update and deletion propagation rules, including reindex verification and stale-citation handling.
Partnered with security to test access boundaries before generation and designed abstention behaviour when supporting evidence was insufficient.
AI projects
Freshness-aware retrieval release
Compared chunking and ranking changes against relevance and source-age criteria.
Investigated retrieval regressions independently of answer wording before promoting an index.
Deletion propagation rehearsal
Tested source removal, cached results and changed permissions across the retrieval path.
Documented reconciliation checks and ownership for incomplete index updates.
Education
Bachelor's degree in a relevant discipline — replace with your qualification
Example Institute (fictional) · 2018–2022
Replace with your actual institution, dates, coursework and qualification. Remove anything you cannot substantiate.
SHOW YOUR CONTRIBUTION
A stronger 2–5 years bullet
TOO VAGUE
Eliminated hallucinations with RAG.
MORE USEFUL EVIDENCE
Owned hybrid retrieval with labelled relevance gates, source-deletion checks and abstention when retrieved evidence was insufficient.
Use this structure only for work you actually did. Add measured results only when you can explain the baseline, evaluation method and workload. The fictional examples do not supply fabricated performance percentages for your application.
5+ YEARS · FICTIONAL SAMPLE
RAG Engineer: 5+ years
Architecture, trade-offs, technical influence and sustained operational ownership.
Example Technology Studio (fictional) · Bengaluru, India · 2022–2026
Defined source-onboarding contracts covering ownership, permissions, freshness, deletion and permitted downstream use.
Led a federated-versus-centralized search decision using representative queries and operational failure scenarios.
Established retrieval evaluation reviews and guided teams through access-boundary incidents and index migrations.
Search / Software Engineer
Example Digital Workshop (fictional) · Bengaluru, India · 2018–2022
Maintained searchable document APIs and relevance test fixtures.
Investigated stale-index results and documented source reconciliation procedures.
AI projects
Governed knowledge onboarding
Designed acceptance checks that reject sources without a clear owner or deletion process.
Separated common ingestion interfaces from domain-specific relevance judgments.
Cross-index migration strategy
Planned shadow-query comparisons and staged source cutovers with reconciliation evidence.
Defined rollback ownership and exceptions for sources with incompatible access models.
Education
Bachelor's degree in a relevant discipline — replace with your qualification
Example Institute (fictional) · 2014–2018
Replace with your actual institution, dates, coursework and qualification. Remove anything you cannot substantiate.
SHOW YOUR CONTRIBUTION
A stronger 5+ years bullet
TOO VAGUE
Unified all company knowledge into one perfect chatbot.
MORE USEFUL EVIDENCE
Defined knowledge-source contracts and a staged cross-index migration with access-policy, relevance and deletion verification.
Use this structure only for work you actually did. Add measured results only when you can explain the baseline, evaluation method and workload. The fictional examples do not supply fabricated performance percentages for your application.
Put the example in context
Use the career guide to identify skill gaps, then compare your evidence with current job requirements. Keep completed work distinct from proposed projects, and choose experience based on responsibility rather than keywords.