RohanChavan_

Problem

Amazon and Together AI gave 10 teams a pretrained code-gen model and one job: make it resist prompt-injection and jailbreak attempts without over-refusing legitimate coding help. Teams went head-to-head in adversarial tournaments.

What I did

• Built the attack taxonomy. Python vulnerability classes and adversarial patterns. Drove how the team generated training data across the project.
• Agent-driven attack simulations. Attacker agent probes the defender, logs what gets through, pushes those failures back as training data for the next round. Cut hand-curating attacks out of the loop.
• Adversarial corpus at scale. The attack-side data that fed the fused defender model and moved our Tournament scores.
• Team placed 1st in Tournament 2, 2nd in Tournament 1, against Claude-3.7 Sonnet and CodeLlama-70B baselines.

Problem

Different agent roles in AutoUnify's coding-agent pipeline needed different model behavior. Worker agents needed strong code generation and task completion. QA agents needed strict validation and deterministic output. Using the same model + prompt everywhere was unreliable, and prompt/parameter changes were landing in production without a way to tell whether they actually helped.

What I did

• LLM benchmarking microservice on FastAPI + PostgreSQL. Stored benchmark runs, prompt versions, model outputs, p50/p95 latency, cost per 1K tokens, and schema-validation results. Exposed scoring data through REST APIs that orchestration logic consumed to pick a model + config per agent role.
• Role-specific prompt + parameter packs for worker and QA agents. Standardized temperature, top-p, max tokens, and schema strictness. Cut manual prompt-tuning effort by ~25%.
• Regression-safe CI/CD via Pytest, lint, JSON-schema checks, and benchmark thresholds in GitHub Actions. Blocked degraded configs before merge and improved merge velocity by ~20%.
• Observability for model selection: p50/p95 latency, cost/1K tokens, quality score, schema-adherence rate, and regression status logged per benchmark run.

Problem

U.S. jurisdictions trying to improve housing affordability have no good way to know which policies fit their local context. This system builds that match: given a jurisdiction's demographic and economic profile, predict which of the three policy classes it is likely to adopt.

What I did

• LLM + RAG prediction pipeline over ChromaDB (6,809 chunks, all-MiniLM-L6-v2 embeddings). Two-pass retrieval: k=20 first pass, k=5 reranked. Llama 3.3 70B via Together AI with Groq fallback. Three policy classes: Density Bonus, ADU, Affordable Dwelling Unit Ordinances.
• Community profile system segmenting jurisdictions into five types (rural low-income, urban high-cost, college town, suburban growing, others) to ground predictions in local context.
• BPS data integration via flat-file parsing of the Building Permit Survey. Wage data sourced manually via BLS where APIs returned null.
• 95 tests passing, grounding checks passing on the held-out eval set, BAD_COMPARABLE detection implemented. FastAPI backend on LangChain with evaluation across demographic slices.

Problem

50+ teams, 36 hours, a brief to build healthcare infrastructure. We chose autonomous microservices to prove we could decompose and reassemble a real workflow under time pressure.

What I did

• 6 autonomous microservices coordinating via JSON-RPC 2.0 with automated workflow triggers.
• Deployed to AWS EC2 and validated 12 end-to-end bookings.
• 4th place + honorable mention among 50+ competing teams.

Problem

Employee onboarding and permission ops were manual, slow, and error-prone. I owned three FastAPI apps replacing the worst offenders.

What I did

• Shipped 3 FastAPI apps with role-based workflows. Saved 200+ hours/month across 400+ employees.
• Cut response times from 30s to under 10s via Redis caching + async I/O while handling 1K+ daily interactions.
• Observability dashboards for latency, error rate, throughput across all three services.