Grounded generation, safety boundaries and quality-cost trade-offs. 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
Generative AI Engineer: Fresher
Education, portfolio projects and foundational skills; no invented employment.
Bengaluru, India · alex@example.com · example.com/portfolio
Professional summary
Entry-level generative AI engineer with portfolio work in evidence-backed drafting and prompt regression tests. Can explain unsupported claims, input boundaries and human review, and distinguishes a local prototype from a deployed product.
Generated draft summaries from permitted source passages and required citations for source-backed claims.
Added a review step for unsupported statements and evaluated coverage separately from writing fluency.
Prompt regression harness
Compared prompt variants on a fixed set of normal, ambiguous and adversarial inputs.
Reported task failures, latency and token usage without treating a model's self-assessed confidence as proof.
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
Created a fully autonomous AI content platform.
MORE USEFUL EVIDENCE
Built a source-grounded drafting prototype with citation checks, prompt regression tests and reviewer approval before publication.
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
Generative AI Engineer: 1–2 years
Specific team contributions, supervised releases and practical troubleshooting.
Bengaluru, India · alex@example.com · example.com/portfolio
Professional summary
Generative AI engineer implementing reviewed prompts, validation and feedback capture for an existing drafting tool. Focuses on reproducible defects and clear approval boundaries rather than describing generated text as verified fact.
Technical skills
Python, LLM APIs, Prompt versioning, JSON validation, Unit tests, Feedback labelling, Git, API integration
Experience
Associate Generative AI Engineer
Example Technology Studio (fictional) · Bengaluru, India · 2025–2026
Implemented a source-summary prompt behind an existing feature flag and added checks for missing citations.
Turned reviewer feedback into labelled regression cases, keeping writing preference separate from factual error.
Added timeout handling and redacted diagnostics under the team's existing logging policy.
AI projects
Citation validation contribution
Checked generated source references against the passages supplied to the model.
Flagged missing or invalid references for review without claiming the check proves every statement true.
Reviewer feedback fixtures
Grouped feedback into factual, coverage and style issues with reproducible sample inputs.
Replayed the fixtures before a reviewed prompt update.
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
Made AI content completely reliable.
MORE USEFUL EVIDENCE
Added citation checks and reviewer-labelled regression fixtures to a feature-flagged drafting workflow.
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
Generative AI Engineer: 2–5 years
End-to-end ownership, production evaluation and cross-team delivery.
Bengaluru, India · alex@example.com · example.com/portfolio
Professional summary
Generative AI engineer owning source-grounded workflows through evaluation and rollout. Balances answer coverage, review effort, latency and provider cost using documented tests and explicit handling of incomplete evidence.
Example Technology Studio (fictional) · Bengaluru, India · 2022–2026
Owned a report-drafting workflow that separated retrieved evidence, generated claims and reviewer-approved output.
Compared provider and prompt configurations using a fixed task set, error taxonomy and measured usage rather than fluency alone.
Implemented bounded fallback and cancellation behaviour, with alerts for review-queue growth and incomplete source retrieval.
AI projects
Quality-cost routing experiment
Evaluated a small-model route against a stronger-model baseline on declared task categories.
Kept ambiguous cases on review and documented categories where cheaper routing was unacceptable.
Draft approval state machine
Preserved the evidence and prompt version associated with each draft through revision and approval.
Tested that changed source material invalidates stale approval.
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
Reduced AI costs with no quality loss.
MORE USEFUL EVIDENCE
Evaluated quality-cost routing on fixed task categories and retained review for ambiguous or unsupported drafts.
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
Generative AI Engineer: 5+ years
Architecture, trade-offs, technical influence and sustained operational ownership.
Bengaluru, India · alex@example.com · example.com/portfolio
Professional summary
Senior engineer setting technical direction for generative AI products across teams. Uses evaluation evidence and data constraints to decide where generation adds value, and builds shared release, provider-change and incident practices.
Technical skills
Generative AI architecture, Evaluation strategy, Provider abstraction, Data governance, Threat modelling, Total cost analysis, Technical mentoring
Experience
Principal Generative AI Engineer
Example Technology Studio (fictional) · Bengaluru, India · 2022–2026
Defined a shared generation gateway with versioned request contracts, data-handling tiers and explicit service ownership.
Led provider-change reviews covering quality regressions, legal-use constraints, operational recovery and cost assumptions.
Guided teams toward retrieval or deterministic processing when generation lacked a defensible quality advantage.
Software Engineer · Content Systems
Example Digital Workshop (fictional) · Bengaluru, India · 2018–2022
Built review and publication workflows with auditable state transitions.
Maintained API compatibility and incident runbooks for source-processing services.
AI projects
Provider migration readiness framework
Designed representative task suites and rollback triggers before changing model revisions.
Documented exceptions and accountable approvers for workflows with consequential outputs.
Generation service boundary redesign
Separated reusable transport and observability from product-specific prompts and evaluations.
Set deprecation and compatibility rules so shared tooling did not hide application-level risk.
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
Led the organization's generative AI transformation.
MORE USEFUL EVIDENCE
Defined a shared generation gateway and provider-change gates covering task quality, data boundaries, recovery and accountable ownership.
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.