Agentic AI & Backend Bullet Auditor

Student Presets:

Project Diagnostic Bench

Raw Score: 42/100

Project Title

Draft Resume Bullets (1 per line)

Target Engineering Focus Signals

Architecture Evidence Stack (Click to Inject)

Detected Gaps in Current Draft

Missing quantitative evaluation benchmark (accuracy, hallucination rate, or retrieval precision)

No production failure modes (circuit breakers, rate limiting, token budget fallbacks)

Unquantified backend throughput and latency (p95/p99 latency, caching layer)

Abstract algorithm claim without operational integration

Production-Engineered Bullets

Internship Ready

Routing & Fault Tolerance Reliability

Architected multi-agent customer query router in FastAPI and LangGraph, implementing schema-validated function calling and dynamic fallback to smaller local SLMs during API outages.

Retrieval & Latency p95 Optimization

Engineered two-tier RAG retrieval using pgvector with hybrid BM25 search, reducing context window tokens by 38% and dropping p95 retrieval latency from 1.4s to 310ms.

Automated Rigor & Evals Evaluation Suite

Built automated evaluation harness over 450 synthetic edge cases measuring retrieval recall and tool hallucination rate, achieving 94.2% task completion fidelity.

Scale & Token Economics Cost Reduction

Integrated Redis semantic response cache and token rate limiter, cutting third-party model inference costs by 47% under simulated 120 req/sec load.

### Multi-Agent Customer Support Assistant - Architected multi-agent customer query router in FastAPI and LangGraph, implementing schema-validated function calling and dynamic fallback to smaller local SLMs during API outages. - Engineered two-tier RAG retrieval using pgvector with hybrid BM25 search, reducing context window tokens by 38% and dropping p95 retrieval latency from 1.4s to 310ms. - Built automated evaluation harness over 450 synthetic edge cases measuring retrieval recall and tool hallucination rate, achieving 94.2% task completion fidelity. - Integrated Redis semantic response cache and token rate limiter, cutting third-party model inference costs by 47% under simulated 120 req/sec load.

Audit Rubric Matrix: Toy Wrapper vs Production Signal

Evaluation Pillar	Typical 5th Sem Student Bullet (High Rejection)	Optimized Systems Engineering Alternative	Why Recruiters Reject The Former
Evals & Hallucinations	"Used LangChain and OpenAI to answer queries with 95% accuracy."	"Constructed 450-scenario eval harness with RAGAS, tracking context precision and reducing hallucination to <2.1%."	No one achieves "95% accuracy" on open-ended LLMs without an automated ground-truth eval dataset.
Latency & Throughput	"Used ChromaDB and embeddings to do fast semantic search."	"Configured HNSW indexing in pgvector with Redis semantic cache, dropping p95 latency from 1.8s to 240ms."	"Fast" is meaningless. Teams want percentiles (p50/p95), cache hit rates, and embedding dimension costs.
Failure Modes & Resilience	"Connected agent tools with OpenAI function calling."	"Engineered Pydantic schema validation with exponential backoff and SLM fallback on rate limit / tool error."	External LLM APIs routinely fail, throttle, and hallucinate parameters; code without fallbacks won't survive production.