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Sudhir Vissa

Machine-learning engineer focused on the parts that actually decide whether ML works in production — evaluation, calibration, thresholds, drift, identity/fraud signals, serving, and the tooling around them. Also building production agentic systems: tool loops, MCP servers, streaming parsers, and agent memory.

Blog · LinkedIn

Upstream contributions

Bug fixes shipped to production ML/LLM libraries. Open PRs tracked at PORTFOLIO.md. Table below updates automatically each day — merged PRs appear here once landed.

PR Repository Description Merged
(none merged yet — 10 open PRs in review)

Writing

Technical posts on production ML/LLM systems.

Post Tags
Your Fraud Model's Scores Are Not Probabilities calibration · production ML · fraud
RAG Retrieval Isn't a Similarity Problem RAG · IR metrics · NDCG · MRR
Running an LLM Gateway in Production LLM · rate limiting · cost · caching
Streaming LLMs in Production: The Edge Cases That Break Your App streaming · SSE · LangChain · production
Fine-Tuning vs. Prompting: A Decision Framework That Doesn't Lie to You fine-tuning · LoRA · RAG · prompting

Also on LinkedIn.

Agentic & LLM infrastructure

Repo What it does
tool-loop Correct agentic tool-use loop: parallel dispatch, error isolation, auto-schema from Python functions
mcp-quickserver MCP server template: tools, resources, and prompts with stdio and SSE transports
stream-parse Parse streaming LLM output: incremental JSON, markdown blocks, tool-call deltas, SSE events
agent-scratchpad Persistent vector memory for agents: embed, store, retrieve by cosine similarity
prompt-cache-bench Benchmark prompt caching: cache hit rate, latency delta, cost savings with real measurements
llm-eval-lite Assertion-based eval harness for LLM/agent outputs; composite checks (AllOf, AnyOf)
rag-eval RAG pipeline evaluation: chunking strategies, retrieval quality, answer faithfulness
llm-gateway Production Anthropic API proxy: token-bucket rate limiting, retry with backoff, cost tracking
rag-demo End-to-end RAG demo: BM25 + tool-loop agent + faithfulness eval + persistent memory

ML evaluation & calibration

Repo What it does
ml-eval-report Binary-classifier eval: metrics, ROC/PR + AUC, threshold sweep, Brier score, ECE
calibrate-ml Probability calibration: Platt scaling, isotonic regression, ECE, reliability diagram
thresholdkit Pick operating thresholds under precision / FPR / cost / expected-value constraints
rankeval NDCG, MRR, AP@K, P@K, R@K — ranking metrics for search, recommendation, RAG

Production ML & data

Repo What it does
featurecheck Feature drift (PSI/KS/chi-squared) + schema/null/dtype validation
idgraph Identity/entity graphs from shared signals; surface synthetic-identity rings + risk scoring
pii-redactor Detect & redact PII (email, phone, SSN, IP, Luhn-validated cards); custom patterns
capture-qa Image capture-quality gates (sharpness, exposure, resolution)
modelcard-gen Generate Model Card markdown from a JSON config

Systems & infrastructure

Repo What it does
tps-bench HTTP throughput & p50/p90/p99 latency benchmark for serving endpoints; warmup + JSON output
cmsketch Count-Min Sketch: approximate counts over high-cardinality streams; merge + serialization

Background

20+ years across devices, cloud, and ML — biometrics & sensing at Motorola/Google/Lenovo, real-time services at Amazon Alexa scale, ML-platform work at SpotHero and Apple (feature pipelines, scoring infrastructure, model monitoring, data-science tooling). ~50 granted patents.

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