INITIALIZING_PORTFOLIO
AJ
Forward Deployed Engineer
LIVE
Open to Full-Time · 2026
ATHARVA
JOSHI
3.73 GPA
MS Data Science
SUNY Buffalo
♞ Chess 2000
SYS TIME: --:--:-- UTC
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01 · The Human
About01
I'm a Forward Deployed Engineer who builds generative-AI systems that survive contact with production. My focus is the hard last mile: taking LLM agents, RAG pipelines, and agentic workflows from prototype to reliable, governed, real-world deployment. I embed with the problem, map chaotic enterprise data into something a model can actually reason over, and ship systems that stakeholders trust, backed by evaluation, guardrails, and audit trails. Recent work spans healthcare AI, clinical-data harmonization, and open-source contributions across the Microsoft, Google, and Goldman Sachs ecosystems.
// Always Supporting
Man United
Real Madrid
India Cricket
Buffalo Bills
// Over the Board
// Right Now
Reading
Me Before You
Jojo Moyes
Listening
The Night We Met
Lord Huron
▶ NOW PLAYING
Watching
Grave of the Fireflies
Isao Takahata · 1988
// FUEL
Cristiano Ronaldo
Football
"Hard work beats talent"
Novak Djokovic
Tennis
"Believe in yourself"
Kobe Bryant
Basketball
"The details are not the details"
Bobby Fischer
Chess
"Chess is life"
// Local Time · New York
02 · Selected Works
Projects That Ship02
PROJECT_01 / 06 · FLAGSHIP
eligible
Patient-to-trial matching on harmonized clinical data. Harmonizes multi-source EHR into the OMOP CDM (dbt), converts free-text trial eligibility into structured logic, and runs audited agentic matching (LangGraph + DeepSeek) with per-criterion evidence. PHI redaction, RBAC, a tamper-evident audit chain, FHIR output, and a Next.js review UI.
610
Structured Criteria
25
Tests Passing
SHA-256
Audit Chain
MIT
Open Source
# eligible patient-to-trial matching
$ make all
→ Harmonize Synthea + legacy CSV into OMOP CDM (dbt)
→ 30 trials · 610 structured eligibility criteria
→ Agentic match: phenotype → retrieve → adjudicate
→ Verdict per criterion: eligible | ineligible | needs_review
$ pytest -q
✓ 25 passed · audit chain · PHI redaction · matching
HuggingFace Demo
PROJECT_02 / 06 · PUBLISHED
finreg-ml
Regulation-aware ML pipeline for finance. GovernedModel, SHAP explainability, fairness audits, EU AI Act compliance, drift detection (KS+PSI), consolidated reports. Published on PyPI.
46
Tests Passing
v0.2.0
PyPI Published
10
Modules
MIT
Open Source
# finreg-ml v0.2.0
$ pip install finreg-ml
Successfully installed finreg-ml-0.2.0
$ python -c "from finreg import GovernedModel; print(GovernedModel.__doc__)"
EU AI Act compliant ML pipeline with SHAP explainability,
fairness audits, drift detection (KS + PSI), and auto-reports.
Supports: scikit-learn estimators | 10 modules | 46 tests
$ pytest tests/ -ra
✓ 46 passed in 4.31s
AI Agent
PROJECT_03 / 06 · LIVE DEMO
clarify
Production LLM agent that turns a free-text brief into a validated BA artifact pack. Multi-step reasoning with a self-correcting clarification loop, typed IDs, RACI matrix, traceability. Llama 3.3 70B fallback chain via OpenRouter.
LLM AgentTool Calling
# clarify
$ # input: "vendor invoice approval tool"
→ Agent asks 6 clarifying questions before assuming
→ Restates assumptions, then ships artifact pack
→ Typed IDs (BR/SR/FR/NFR/TR/TC) · RTM · RACI register
✓ First-draft turnaround: ~6 hours → under 5 minutes
PROJECT_04 / 06
talent-rag
Production-grade RAG system for talent intelligence. Hybrid retrieval (dense plus sparse), cross-encoder reranking, and ensemble generation over a FAISS index, served through FastAPI with a Streamlit console.
Hybrid
Retrieval
Cross-Enc
Reranking
FAISS
Vector Store
MIT
Open Source
# talent-rag talent intelligence RAG
$ python -m talent_rag.query "senior ML engineer"
→ Hybrid retrieval: dense (FAISS) + sparse (BM25)
→ Cross-encoder rerank → top-k candidates
→ Ensemble generation → grounded, cited answer
$ uvicorn app:api · streamlit run ui.py
✓ FastAPI service + Streamlit console
CLI Framework
PROJECT_05 / 06
agenteval
AI agent evaluation framework with AgentRunner, LLM-as-judge scoring (OpenAI + Anthropic), CLI, safety checks for PII & prompt injection, multi-format export.
LLM-as-judgeSafety Checks
# agenteval v0.2.0
$ agenteval run --agent my_agent.py --suite evals/suite.json
→ Loading 11 modules · 62 tests passing
→ LLM Judge: claude-sonnet-4 (Anthropic)
→ Safety: PII scan ✓ · Injection scan ✓
→ Export: JSON · CSV · Markdown
✓ Report saved: eval_results.json
Backtest Engine
PROJECT_06 / 06
crypto-stat-arb
Statistical arbitrage engine. Engle-Granger cointegration, Johansen basket trading, Kalman filter hedge ratios, walk-forward backtesting, regime detection, paper trading via Kraken API.
Kraken APIKalman Filter
# crypto-stat-arb v0.1.0
$ python -m cryptoarb.backtest --pair BTC-ETH --window 90d
→ Engle-Granger cointegration test
→ Kalman filter hedge ratio (rolling)
→ Walk-forward backtest · regime detection
→ Market neutral confirmed (BTC corr ≈ 0.03)
✓ 107 tests · paper trading via Kraken API
03 · Open Source DNA
Building in Public03
0+
COMBINED GITHUB STARS ACROSS CONTRIBUTIONS
Merged Pull Requests
#549
#776
#786
#1410
Open Pull Requests
#345
#113
#9809
#512
#364
04 · Tech Stack
The Toolbox04
// Foundation Models
Claude Opus 4.7Sonnet 4.6Haiku 4.5GPT-5GPT-4oo3Gemini 2.5DeepSeek R1Llama 4Grok 4
// Agents & Orchestration
LangGraphLangChainCrewAILlamaIndexMulti-Agent SystemsAgentic WorkflowsClaude CodeClaude Skills
// RAG & Retrieval
RAG PipelinesGraphRAGSemantic SearchText-to-SQLVector SearchDocument Intelligence
// Vector DBs & Graphs
PineconepgvectorNeo4jKnowledge Graphs
// AI Platforms & APIs
Anthropic APIMCPOpenAI RealtimeAzure OpenAIAWS BedrockGoogle Vertex AIHugging Face Hub
// Backend & APIs
PythonSQLFastAPIREST APIsMicroservicesSecure API Integration
// Eval, Safety & Governance
Guardrails AINeMo GuardrailsRagasTruLensLLM EvaluationRBACPHI MaskingHIPAA
// Cloud, DevOps & Data
AWSAzureGCPDockerKubernetesGitHub ActionsCI/CDPostgreSQLMongoDB
◉ Currently Learning
→ Reinforcement Learning → CUDA Programming → Rust → Transformer Fine-tuning → JAX/XLA → Options Pricing → RLHF → → Reinforcement Learning → CUDA Programming → Rust → Transformer Fine-tuning → JAX/XLA → Options Pricing → RLHF →
05 · Education
Education05
MS · STEM · GPA 3.73
State University of New York at Buffalo
MS, Data Science
STEM-designated Data Science program combining rigorous statistical modeling with production ML engineering, quantitative finance, and real-world systems work.
Aug 2024 to Dec 2025 · Buffalo, NY
★ Achievement
GPA 3.73 · 5 open source projects · 11 PRs across Microsoft, Google, Goldman Sachs ecosystems
Focus Areas
Machine Learning, Statistical Modeling, Quantitative Finance, MLOps, AI Evaluation, Data Engineering
06 · Let's Connect
Let's Build Something.
Open for full-time roles starting 2026 · Data Scientist · ML Engineer · Quant Developer · AI Engineer