Aditya Saini — Portfolio

Projects

Certified

Self-directed project

Frontend Engineering with Prediction Game

Junior Front-End Engineer · Software Engineering · October 2026

92/ 100

A ProoV case study · educational project, not employment

Built a complete World Cup prediction game end to end: rendered the fixture board, scored picks with the exact 5/2/0 rule, computed team-strength lookups from real results, and used them to drive a bot predictor. Also implemented a persistent ranked leaderboard and shipped the finished game to a live Netlify URL.

Graded against

Scoring engine correctness30%
Prediction model genuinely from real data25%
Game completeness and leaderboard soundness25%
Shipped and shareable: live deployed URL20%

Passed · pass mark 60/100

What stood out6

Rendered each fixture as a card with two numeric inputs and visible team names
Implemented scorePick with correct exact/result-only/wrong-result handling, including draws
Computed team strengths from real results and used both teams' strengths in botPredict
Persisted and reloaded leaderboard state under one key with descending sort
Leakage-Free Feature Engineering
Honest Model Evaluation

The work I submitted17 tasks

9 tasks · 11 lines · JavaScript · 8 written answers · 699 words

Lab Orientation678 characters
Act1 Fixtures FormJavaScript · 3.8k characters
Act2 Scoring FunctionJavaScript · 2.3k characters
Act3 Compute Strength108 characters
Act3 House Bot108 characters
Example Save Rank ReadonlyJavaScript · 3.3k characters
Act4 LeaderboardJavaScript · 2 lines
Act5 Assemble2 lines
Final Submission91 characters
Lab Orientation66 words
Act1 Fixtures Form143 words
Act2 Scoring Function131 words
Act3 Compute Strength1 word
Act3 House Bot1 word
Example Save Rank Readonly111 words
Act4 Leaderboard138 words
Act5 Assemble108 words

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV

Certified

Self-directed project

Fundamentals of Cybersecurity

Cybersecurity · September 2026

91/ 100

A ProoV case study · educational project, not employment

Built an end-to-end incident response workflow for a phishing-led breach: triaged the lure, traced the attacker through authentication logs, mapped the intrusion to the cyber kill chain, and selected containment actions that cut access before escalation. Also drafted a GDPR Article 33 notification and translated the method to the Continental case with a concrete control recommendation focused on segmentation and DLP.

Graded against

Threat Triage & Detection20%
Log Analysis & Investigation30%
Incident Response & Containment20%
Compliance & Breach Notification15%
Executive Communication & Lessons15%

Passed · pass mark 60/100

What stood out6

Caught all four phishing red flags, including sender, link, attachment, and urgency
Identified the impossible-travel sign-in and tied it to the compromised account path
Chose a containment order that cuts access before more disruptive actions
Anchored the real-case recommendation to segmentation and DLP at the exfiltration stage
Leakage-Free Incident Triage
Evidence-Based Log Investigation

The work I submitted18 tasks

10 tasks · 6.4k characters · 8 written answers · 314 words

Phishing Triage186 characters
Killchain Map244 characters
Log Triage170 characters
Ioc Extraction176 characters
Containment Plan373 characters
Breach Notification944 characters
Incident Report2.3k characters
Recommendations648 characters
Real Case Analysis817 characters
Teach Back552 characters
Phishing Triage8 words
Killchain Map12 words
Log Triage9 words
Ioc Extraction8 words
Containment Plan14 words
Breach Notification103 words
Incident Report147 words
Recommendations13 words

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV

Certified

Self-directed project

Supply Chain Optimization with SAP Business AI

Business AI Associate · AI / ML · September 2026

94/ 100

A ProoV case study · educational project, not employment

Built a supply-chain rescue plan for a fictional SAP manufacturer by isolating the true bottleneck, a single-sourced critical component with about two weeks of cover, and translating it into a Joule-style copilot that surfaces the next best action. The work includes explicit human-approval guardrails, an honest euros-saved estimate, an India transfer check, and a concise board pitch with a bounded pilot ask.

Graded against

Problem framing & business sense30%
Business-AI solution design & guardrails30%
ROI and decision quality25%
Executive communication15%

Passed · pass mark 60/100

What stood out6

Correctly rejected the loud port delay and focused on the single-sourced bottleneck with ~2 weeks of cover
Defined a Joule-style copilot that surfaces the next best action and keeps high-stakes decisions with a human
Applied the low-value, reversible, high-confidence rule correctly and caught the cheap-but-irreversible trap
Built an honest payback case that states 3 recovered days plus avoided expedite cost without overclaiming
Leakage-Free Problem Diagnosis
Human-in-the-Loop AI Guardrail Design

The work I submitted19 tasks

11 tasks · 7.2k characters · 8 written answers · 548 words

SAP Brand Sentiment Pre182 characters
Trust Pledge135 characters
Diagnose Break331 characters
Problem Statement973 characters
Copilot Spec700 characters
Guardrails Sort1.7k characters
ROI Payback586 characters
India Transfer565 characters
Board Pitch1.4k characters
Teach Back434 characters
SAP Brand Sentiment Post188 characters
SAP Brand Sentiment Pre14 words
Trust Pledge20 words
Diagnose Break23 words
Problem Statement139 words
Copilot Spec96 words
Guardrails Sort127 words
ROI Payback60 words
India Transfer69 words

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV

Certified

Self-directed project

AI-Assisted Code Modernization with IBM

Junior Software Engineer · Software Engineering · September 2026

96/ 100

Based on a public IBM case study · not affiliated with IBM

Built and verified a full legacy-code modernization for KM-Waechter: fixed the wear calculation, handled missing service readings safely, repaired the nightly report, corrected a hidden mileage-conversion bug in the helpers, and added the missing regression test. Also delivered a data-driven breakdown-risk analysis that follows the fleet history rather than the naive mileage assumption, with acceptance checks confirming all requirements passed.

Graded against

Fix and extend the repo with the agent35%
Diagnose the bug20%
Direct and audit the AI agent25%
Explain it like an engineer20%

Passed · pass mark 60/100

What stood out6

Correctly identified the whole-number division bug and explained why it suppressed nearly-worn cars to 0% wear.
Caught the quiet helper-module mileage conversion bug that no test covered and fixed it in fleet_utils.py.
Added the missing no-reading fleet report test and confirmed the repo with a full 11/11 acceptance check.
Produced a data-backed analyze.py that explicitly rejects the misleading mileage/age assumption and focuses on the predictive factors in the dataset.
Leakage-Free Bug Diagnosis
Directed AI Code Review

The deliverable

View the codeadityasaini0070 / proov-km-waechter-fix

The work I submitted20 tasks

12 tasks · 3.2k characters · 8 written answers · 126 words

Division Predict71 characters
Diagnose Line130 characters
Diagnose Why159 characters
Scope Prompt66 characters
Audit Findings158 characters
Audit Why356 characters
Acceptance Checklist170 characters
Bob Trial Click74 characters
Template Repo Click82 characters
Bob Ready60 characters
Verify Report71 characters
Exec Pitch1.8k characters
Division Predict1 word
Diagnose Line10 words
Diagnose Why24 words
Scope Prompt1 word
Audit Findings1 word
Audit Why62 words
Acceptance Checklist26 words
Bob Trial Click1 word

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV

Certified

Self-directed project

Amazon Robotics - Black-Friday Fleet Coordination

Software Engineering · August 2026

96/ 100

A ProoV case study · educational project, not employment

Built an end-to-end warehouse fleet coordination prototype for Black Friday surge conditions. The work framed the bottleneck correctly as aisle congestion and dock contention, implemented admissible A* plus a centralized reservation-table cooperative planner with vertex and edge safety, and validated a realistic policy stack for charging and idle placement. The final recommendation was grounded in simulator evidence showing zero collisions and sustained throughput under the chosen policy.

Graded against

Problem framing & operational stakes20%
Algorithmic depth - A* and cooperative coordination30%
Coordination policy realism - deadlock, charging, idle25%
Throughput trade-off analysis15%
Executive pitch to leadership10%

Passed · pass mark 55/100

What stood out6

Implemented A* with an admissible Manhattan heuristic on a 4-connected grid and verified it against optimal path length and runtime comparisons.
Built a reservation table that reserves both vertices and directed edges, preventing same-cell collisions and head-on swaps.
Selected a realistic coordination policy: priority/watchdog replan, staggered charging, and dispersed perimeter idle parking.
Used simulator outputs to identify a concave throughput optimum at 36 robots and tied the final pitch to zero collisions over the full shift.
Leakage-Free Feature Engineering
Honest Model Evaluation

The work I submitted20 tasks

12 tasks · 7.4k characters · Python · 8 written answers · 364 words

Role And Mission115 characters
Amazon Robotics Context459 characters
Grid World Theory104 characters
State Representation Task ValidatorPython · 1.8k characters
State Representation Task341 characters
Phase1 Recap348 characters
Astar Theory124 characters
Astar Implementation Cell3.2k characters
Astar Solution Correct4 characters
Single Unit Challenge464 characters
Phase2 Recap374 characters
Multi Agent Problem111 characters
Role And Mission1 word
Amazon Robotics Context37 words
Grid World Theory1 word
State Representation Task Validator133 words
State Representation Task1 word
Phase1 Recap23 words
Astar Theory1 word
Astar Implementation Cell167 words

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV