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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Nantha Kumar — AI Engineer | Generative AI & LLM Systems</title>
<meta name="description" content="Nantha Kumar — AI Engineer building production LLM systems: agents, RAG, and evals. Generative AI on LangGraph, full-stack Next.js & FastAPI.">
<meta name="author" content="Nantha Kumar">
<link rel="canonical" href="https://nanduu24.github.io/">
<link rel="icon" href="data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%2064%2064'%3E%3Crect%20width='64'%20height='64'%20rx='12'%20fill='%23123A47'/%3E%3Ctext%20x='32'%20y='44'%20font-family='Georgia,serif'%20font-size='30'%20font-weight='700'%20fill='%23B07A22'%20text-anchor='middle'%3ENK%3C/text%3E%3C/svg%3E">
<!-- Open Graph / social link previews -->
<meta property="og:type" content="website">
<meta property="og:url" content="https://nanduu24.github.io/">
<meta property="og:title" content="Nantha Kumar — AI Engineer | Generative AI & LLM Systems">
<meta property="og:description" content="AI Engineer building production LLM systems: agents, RAG, and evals. Generative AI on LangGraph, full-stack Next.js & FastAPI.">
<meta property="og:image" content="https://nanduu24.github.io/professional-photo.jpg">
<meta property="og:image:alt" content="Nantha Kumar">
<meta name="twitter:card" content="summary_large_image">
<meta name="twitter:title" content="Nantha Kumar — AI Engineer | Generative AI & LLM Systems">
<meta name="twitter:description" content="AI Engineer building production LLM systems: agents, RAG, and evals. Generative AI on LangGraph, full-stack Next.js & FastAPI.">
<meta name="twitter:image" content="https://nanduu24.github.io/professional-photo.jpg">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Bricolage+Grotesque:opsz,wght@12..96,500;12..96,700;12..96,800&family=IBM+Plex+Mono:wght@500;600&family=IBM+Plex+Sans:wght@400;500;600&display=swap" rel="stylesheet">
<style>
:root{
--paper:#EDEFF2;
--paper-2:#F6F7F9;
--ink:#0F1418;
--ink-soft:#2C3439;
--petrol:#123A47;
--petrol-deep:#0B222B;
--petrol-soft:#1C4E5E;
--brass:#C08A2E;
--brass-bright:#D69A34;
--mint:#8FE3C4;
--slate:#5D6B72;
--line:#D3D9DD;
--line-dark:rgba(255,255,255,.10);
--display:'Bricolage Grotesque',Georgia,serif;
--sans:'IBM Plex Sans',-apple-system,BlinkMacSystemFont,'Segoe UI',sans-serif;
--mono:'IBM Plex Mono',ui-monospace,Menlo,monospace;
--wrap:1200px;
--gutter:clamp(20px,5vw,64px);
--radius:14px;
--shadow:0 1px 2px rgba(11,34,43,.06),0 12px 34px -18px rgba(11,34,43,.30);
--shadow-lift:0 2px 6px rgba(11,34,43,.10),0 26px 50px -22px rgba(11,34,43,.42);
}
*{box-sizing:border-box}
html{scroll-behavior:smooth}
body{
margin:0;
background:var(--paper);
color:var(--ink);
font-family:var(--sans);
font-size:17px;
line-height:1.65;
-webkit-font-smoothing:antialiased;
text-rendering:optimizeLegibility;
}
a{color:inherit}
img{max-width:100%;display:block}
::selection{background:var(--brass);color:#160E02}
.wrap{max-width:var(--wrap);margin:0 auto;padding:0 var(--gutter)}
:focus-visible{outline:2px solid var(--brass);outline-offset:3px;border-radius:3px}
.skip{
position:absolute;left:-9999px;top:0;z-index:100;
background:var(--brass);color:#160E02;padding:10px 16px;border-radius:0 0 8px 0;font-weight:600;
}
.skip:focus{left:0}
.eyebrow{
font-family:var(--mono);font-size:.74rem;letter-spacing:.16em;text-transform:uppercase;
color:var(--brass);font-weight:600;
}
/* ---------- top bar ---------- */
.bar{
position:sticky;top:0;z-index:40;
background:rgba(11,34,43,.82);
-webkit-backdrop-filter:saturate(160%) blur(12px);
backdrop-filter:saturate(160%) blur(12px);
color:var(--paper-2);
border-bottom:1px solid var(--line-dark);
}
.bar .wrap{display:flex;align-items:center;justify-content:space-between;gap:24px;height:60px}
.bar .mark{
font-family:var(--display);font-weight:800;letter-spacing:-.02em;font-size:1.05rem;
display:flex;align-items:center;gap:10px;
}
.bar .mark .badge{
display:grid;place-items:center;width:30px;height:30px;border-radius:8px;
background:var(--petrol-soft);color:var(--brass-bright);
font-size:.82rem;font-weight:800;border:1px solid rgba(255,255,255,.14);
}
.bar nav{display:flex;gap:8px;font-size:.9rem}
.bar nav a{
text-decoration:none;padding:7px 12px;border-radius:8px;color:#AFC0C6;
transition:color .18s ease,background .18s ease;
}
.bar nav a:hover{color:#fff;background:rgba(255,255,255,.07)}
.bar nav a.active{color:#fff;background:rgba(192,138,46,.18)}
.bar .cta{
text-decoration:none;font-size:.88rem;font-weight:600;padding:8px 16px;border-radius:9px;
background:var(--brass);color:#160E02;border:1px solid var(--brass);
transition:background .18s ease,transform .18s ease;
}
.bar .cta:hover{background:var(--brass-bright);transform:translateY(-1px)}
@media(max-width:820px){.bar nav{display:none}}
/* ---------- hero ---------- */
.hero{
position:relative;overflow:hidden;
background:
radial-gradient(120% 90% at 85% -10%,rgba(143,227,196,.10),transparent 55%),
radial-gradient(90% 70% at -5% 110%,rgba(192,138,46,.16),transparent 55%),
linear-gradient(165deg,var(--petrol) 0%,var(--petrol-deep) 100%);
color:#EDF1F2;
padding:clamp(60px,9vw,116px) 0 clamp(56px,8vw,96px);
}
.hero::before{
content:"";position:absolute;inset:0;pointer-events:none;opacity:.5;
background-image:linear-gradient(rgba(255,255,255,.035) 1px,transparent 1px),
linear-gradient(90deg,rgba(255,255,255,.035) 1px,transparent 1px);
background-size:52px 52px;
-webkit-mask-image:radial-gradient(120% 80% at 30% 0%,#000,transparent 75%);
mask-image:radial-gradient(120% 80% at 30% 0%,#000,transparent 75%);
}
.hero .wrap{position:relative;z-index:1}
.hero-grid{
display:grid;
grid-template-columns:minmax(0,1.5fr) minmax(270px,.82fr);
gap:clamp(32px,6vw,76px);
align-items:center;
}
.status{
display:inline-flex;align-items:center;gap:9px;
font-family:var(--mono);font-size:.78rem;letter-spacing:.04em;
padding:7px 14px;border-radius:999px;
background:rgba(143,227,196,.10);border:1px solid rgba(143,227,196,.34);color:#BFEFDB;
margin:0 0 24px;
}
.status .dot{
width:8px;height:8px;border-radius:50%;background:var(--mint);
box-shadow:0 0 0 0 rgba(143,227,196,.6);animation:pulse 2.4s infinite;
}
@keyframes pulse{
0%{box-shadow:0 0 0 0 rgba(143,227,196,.55)}
70%{box-shadow:0 0 0 9px rgba(143,227,196,0)}
100%{box-shadow:0 0 0 0 rgba(143,227,196,0)}
}
.hero h1{
font-family:var(--display);font-weight:800;
font-size:clamp(2.9rem,6.6vw,4.9rem);
line-height:.95;letter-spacing:-.04em;margin:0 0 16px;
}
.hero h1 .grad{
background:linear-gradient(92deg,#fff 30%,var(--mint));
-webkit-background-clip:text;background-clip:text;color:transparent;
}
.role{
font-size:clamp(1.05rem,1.7vw,1.24rem);color:#9FE0C9;font-weight:500;
margin:0 0 26px;display:flex;align-items:center;gap:12px;flex-wrap:wrap;
}
.role .sep{width:6px;height:6px;border-radius:50%;background:var(--brass)}
.role .mid{color:var(--brass);font-weight:600;opacity:.85}
.role .loc{color:#B9C7CD;font-size:.95em}
.hero p.lede{
max-width:54ch;color:#D2DEE2;font-size:clamp(1rem,1.3vw,1.12rem);margin:0 0 32px;
}
.hero p.lede strong{color:#fff;font-weight:600}
.contact{display:flex;flex-wrap:wrap;gap:11px}
.contact a{
text-decoration:none;font-size:.93rem;font-weight:500;
padding:11px 18px;border-radius:10px;
border:1px solid rgba(255,255,255,.22);color:#E6EDEF;
display:inline-flex;align-items:center;gap:8px;
transition:background .18s ease,border-color .18s ease,transform .18s ease;
}
.contact a:hover{background:rgba(255,255,255,.09);border-color:var(--brass);transform:translateY(-1px)}
.contact a.primary{background:var(--brass);border-color:var(--brass);color:#160E02;font-weight:600}
.contact a.primary:hover{background:var(--brass-bright)}
.contact svg{width:16px;height:16px;flex:none}
/* ---------- photo plate ---------- */
.plate{position:relative}
.plate-inner{
position:relative;aspect-ratio:4/5;border-radius:var(--radius);
background:var(--petrol-soft);border:1px solid rgba(255,255,255,.18);
overflow:hidden;box-shadow:var(--shadow-lift);
}
.plate-inner::after{
content:"";position:absolute;inset:10px;border-radius:8px;
border:1px solid rgba(192,138,46,.6);pointer-events:none;
}
#photo{width:100%;height:100%;object-fit:cover;object-position:center top}
.plate-empty{
position:absolute;inset:0;display:flex;flex-direction:column;align-items:center;justify-content:center;
gap:6px;text-align:center;padding:24px;color:#BBD0D7;
}
.plate-empty span:first-child{font-family:var(--display);font-size:1.5rem;color:#EDF1F2;font-weight:700}
body.has-photo .plate-empty{display:none}
.plate-caption{
margin-top:14px;font-family:var(--mono);font-size:.78rem;color:#9FB3BA;
display:flex;justify-content:space-between;gap:12px;
border-top:1px solid rgba(255,255,255,.14);padding-top:11px;
}
/* ---------- impact stats band ---------- */
.stats{
background:var(--petrol-deep);color:#DCE6E9;border-top:1px solid var(--line-dark);
}
.stats .grid{
display:grid;grid-template-columns:repeat(4,1fr);
}
.stat{
padding:clamp(26px,4vw,40px) clamp(16px,3vw,32px);
border-left:1px solid var(--line-dark);
}
.stat:first-child{border-left:none}
.stat .n{
font-family:var(--display);font-weight:800;letter-spacing:-.03em;
font-size:clamp(1.7rem,3.4vw,2.5rem);color:#fff;line-height:1;
display:flex;align-items:baseline;gap:3px;
}
.stat .n em{font-style:normal;color:var(--brass-bright);font-size:.62em;font-weight:800}
.stat .l{margin-top:9px;font-size:.86rem;color:#9FB3BA;line-height:1.4}
@media(max-width:760px){
.stats .grid{grid-template-columns:1fr 1fr}
.stat:nth-child(3){border-left:none}
.stat:nth-child(n+3){border-top:1px solid var(--line-dark)}
}
/* ---------- sections ---------- */
section{padding:clamp(58px,7.5vw,96px) 0}
.sec-head{margin:0 0 clamp(30px,4vw,48px)}
.sec-head .eyebrow{display:block;margin-bottom:10px}
.sec-head h2{
font-family:var(--display);font-weight:800;
font-size:clamp(1.7rem,3.2vw,2.5rem);letter-spacing:-.035em;margin:0;
display:flex;align-items:baseline;gap:16px;
}
.sec-head h2 .count{font-family:var(--mono);font-size:.8rem;color:var(--slate);font-weight:500;letter-spacing:0}
.sec-sub{color:var(--slate);margin:10px 0 0;max-width:60ch}
.section-alt{background:var(--paper-2);border-top:1px solid var(--line);border-bottom:1px solid var(--line)}
/* ---------- work cards ---------- */
.cards{display:grid;grid-template-columns:1fr 1fr;gap:clamp(18px,2.4vw,26px)}
@media(max-width:800px){.cards{grid-template-columns:1fr}}
.card{
position:relative;display:flex;flex-direction:column;
background:#fff;border:1px solid var(--line);border-radius:var(--radius);
padding:clamp(24px,3vw,32px);box-shadow:var(--shadow);overflow:hidden;
transition:transform .22s ease,box-shadow .22s ease,border-color .22s ease;
}
.card::before{
content:"";position:absolute;left:0;top:0;bottom:0;width:3px;
background:linear-gradient(var(--brass),var(--petrol-soft));
transform:scaleY(0);transform-origin:top;transition:transform .28s ease;
}
.card:hover{transform:translateY(-4px);box-shadow:var(--shadow-lift);border-color:#C2CDD2}
.card:hover::before{transform:scaleY(1)}
.card-top{display:flex;align-items:flex-start;justify-content:space-between;gap:14px;margin-bottom:6px}
.card h3{
font-family:var(--display);font-size:1.32rem;font-weight:700;letter-spacing:-.02em;margin:0;line-height:1.15;
}
.card h3 a{text-decoration:none;color:inherit;transition:color .18s ease}
.card h3 a:hover{color:var(--petrol-soft)}
.card h3 .ext{color:var(--brass);font-size:.82em;font-weight:600;transition:transform .18s ease;display:inline-block}
.card h3 a:hover .ext{transform:translate(2px,-2px)}
.tag-live{
flex:none;font-family:var(--mono);font-size:.68rem;letter-spacing:.06em;text-transform:uppercase;
padding:5px 10px;border-radius:999px;font-weight:600;white-space:nowrap;
background:rgba(143,227,196,.16);color:#0E6b4f;border:1px solid rgba(18,58,71,.14);
}
.tag-flag{
flex:none;font-family:var(--mono);font-size:.68rem;letter-spacing:.06em;text-transform:uppercase;
padding:5px 10px;border-radius:999px;font-weight:600;white-space:nowrap;
background:rgba(192,138,46,.14);color:#8A5E14;border:1px solid rgba(192,138,46,.28);
}
.card .meta{color:var(--slate);font-size:.93rem;margin:0 0 16px}
.card ul{margin:0;padding-left:18px}
.card li{margin-bottom:10px;font-size:.97rem;color:var(--ink-soft)}
.card li:last-child{margin-bottom:0}
.num{font-family:var(--mono);color:var(--petrol);font-weight:600}
.chips{display:flex;flex-wrap:wrap;gap:7px;margin-top:18px;padding-top:16px;border-top:1px solid var(--line)}
.card .chips{margin-top:auto}
.chip{
font-family:var(--mono);font-size:.76rem;color:var(--petrol);
background:var(--paper);border:1px solid var(--line);border-radius:7px;padding:4px 9px;white-space:nowrap;
}
/* ---------- research feature ---------- */
.research{
background:linear-gradient(160deg,#fff, #F6F7F9);
border:1px solid var(--line);border-radius:var(--radius);
padding:clamp(26px,3.6vw,44px);box-shadow:var(--shadow);
display:grid;grid-template-columns:minmax(0,.85fr) minmax(0,1.9fr);gap:clamp(24px,4vw,52px);
}
@media(max-width:820px){.research{grid-template-columns:1fr;gap:22px}}
.research h3{font-family:var(--display);font-size:1.4rem;font-weight:700;letter-spacing:-.02em;margin:0 0 6px}
.research .meta{color:var(--slate);font-size:.95rem;margin:0}
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<div class="status reveal"><span class="dot"></span> Open to AI Engineer & Generative AI roles</div>
<h1 class="reveal d1">Nantha Kumar</h1>
<p class="role reveal d1">
AI Engineer <span class="mid">·</span> Software Engineer
<span class="sep"></span>
<span class="loc">Irving, TX · open to relocation</span>
</p>
<p class="lede reveal d2">
I build production LLM systems — <strong>agents, RAG, and evals</strong> — that hold up
outside a demo. Generative AI on LangGraph with grounded retrieval and multi-provider
routing, full-stack on Next.js and FastAPI, and open-source work merged into
<strong>Microsoft PyRIT</strong>.
</p>
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<div class="plate-empty">
<span>Nantha Kumar</span>
<span>AI Engineer</span>
</div>
</div>
<div class="plate-caption">
<span>Irving, TX</span>
<span>Open to relocation</span>
</div>
</div>
</div>
</section>
<section class="stats" aria-label="Impact at a glance">
<div class="wrap">
<div class="grid">
<div class="stat reveal">
<div class="n">4</div>
<div class="l">LLM projects built end to end</div>
</div>
<div class="stat reveal d1">
<div class="n">3</div>
<div class="l">Open-source PRs to Microsoft & Keras</div>
</div>
<div class="stat reveal d2">
<div class="n">1,014</div>
<div class="l">Documents indexed for grounded retrieval</div>
</div>
<div class="stat reveal d3">
<div class="n">720</div>
<div class="l">Core samples modeled in ML research</div>
</div>
</div>
</div>
</section>
<section id="work" class="section-alt">
<div class="wrap">
<div class="sec-head reveal">
<span class="eyebrow">Selected work</span>
<h2>Things I've built <span class="count">4 projects</span></h2>
<p class="sec-sub">LLM systems and applied ML — built end to end, from retrieval and routing to auth, billing and deploy.</p>
</div>
<div class="cards">
<article class="card reveal">
<div class="card-top">
<h3><a href="https://mindful-ai-omega.vercel.app" target="_blank" rel="noopener">Mindful AI <span class="ext">↗</span></a></h3>
<span class="tag-live">Live</span>
</div>
<p class="meta">Agentic AI platform with voice and memory</p>
<ul>
<li>Built a six-stage LangGraph agent with provider-agnostic routing across Claude, Gemini 2.0 Flash and Llama 3.3 70B — switchable through a single environment variable — holding cost near <span class="num">$0.0002</span> per session.</li>
<li>Built RAG memory on Postgres with pgvector and HNSW indexing (768-dim embeddings, custom cosine-similarity SQL) returning results in under <span class="num">200 ms</span>.</li>
<li>Added browser push-to-talk voice via Gemini speech-to-text and Eleven Labs, deployed across Vercel, Hugging Face Spaces and Supabase with LangSmith tracing, Clerk auth and Stripe billing.</li>
</ul>
<div class="chips">
<span class="chip">Next.js</span><span class="chip">FastAPI</span><span class="chip">LangGraph</span><span class="chip">pgvector</span><span class="chip">Claude</span><span class="chip">Gemini</span><span class="chip">Stripe</span>
</div>
</article>
<article class="card reveal d1">
<div class="card-top">
<h3><a href="https://github.com/Sanju970/fleet_intelligence_platform" target="_blank" rel="noopener">FleetIQ <span class="ext">↗</span></a></h3>
<span class="tag-flag">Buildathon '26</span>
</div>
<p class="meta">Grounded document Q&A agent — team project · Dallas, 32-hour build</p>
<ul>
<li>Built a hybrid retrieval agent over <span class="num">1,014</span> fleet documents spanning 12 real carrier form types that answers with citations and abstains when the evidence is missing.</li>
<li>Generated the corpus from 12 templates with realistic entity variation, creating known ground truth so retrieval quality could be measured rather than eyeballed.</li>
<li>Delivered backend, dispatch interface and pitch inside the 32-hour window.</li>
</ul>
<div class="chips">
<span class="chip">LangGraph</span><span class="chip">FastAPI</span><span class="chip">Hybrid RAG</span>
</div>
</article>
<article class="card reveal">
<div class="card-top">
<h3><a href="https://github.com/Nanduu24/jobagent" target="_blank" rel="noopener">Job Agent <span class="ext">↗</span></a></h3>
</div>
<p class="meta">Autonomous job-application pipeline</p>
<ul>
<li>Ingests postings from four job boards, scores them with a heuristic filter followed by an LLM fit review, tailors the resume through a LangGraph workflow and renders the PDF — run end to end against live postings.</li>
<li>Added a fact-verification node that checks every generated claim against a structured profile and blocks invented skills, titles or dates before the document is rendered.</li>
</ul>
<div class="chips">
<span class="chip">Python</span><span class="chip">LangGraph</span><span class="chip">Greenhouse</span><span class="chip">Lever</span><span class="chip">Ashby</span><span class="chip">Workable</span>
</div>
</article>
<article class="card reveal d1">
<div class="card-top">
<h3>AI Career Job Advisor</h3>
</div>
<p class="meta">LLM-powered career SaaS — 3-person Agile team</p>
<ul>
<li>Owned resume-to-job-description matching, ATS scoring and cover-letter generation with schema-validated outputs.</li>
<li>Wrote <span class="num">25+</span> backend tests and contributed to a Selenium end-to-end suite.</li>
</ul>
<div class="chips">
<span class="chip">OpenAI</span><span class="chip">FastAPI</span><span class="chip">Next.js</span><span class="chip">PostgreSQL</span>
</div>
</article>
</div>
</div>
</section>
<section id="opensource">
<div class="wrap">
<div class="sec-head reveal">
<span class="eyebrow">Open source</span>
<h2>Contributions upstream <span class="count">3 PRs</span></h2>
<p class="sec-sub">Fixes and features sent to tools the ML community actually runs — merged into Microsoft PyRIT and vasim, in review at Keras.</p>
</div>
<div class="cards">
<article class="card reveal">
<div class="card-top">
<h3><a href="https://github.com/microsoft/PyRIT/pull/2649" target="_blank" rel="noopener">Microsoft PyRIT <span class="ext">↗</span></a></h3>
<span class="tag-live">Merged</span>
</div>
<p class="meta">LLM red-teaming toolkit · PR #2649</p>
<ul>
<li>Added a phrase-aware PinyinConverter that rewrites Chinese prompts into Pinyin variants, testing whether model safety holds when the same request crosses scripts.</li>
<li>Shipped with unit and determinism tests, documentation and packaging updates — <span class="num">+570 / −49</span> across 10 files.</li>
</ul>
<div class="chips">
<span class="chip">Python</span><span class="chip">LLM security</span><span class="chip">Pytest</span>
</div>
</article>
<article class="card reveal d1">
<div class="card-top">
<h3><a href="https://github.com/microsoft/vasim/pull/139" target="_blank" rel="noopener">Microsoft vasim <span class="ext">↗</span></a></h3>
<span class="tag-live">Merged</span>
</div>
<p class="meta">VM autoscaling simulator · PR #139</p>
<ul>
<li>Root-caused an off-by-one in scaling-event counts — a trailing NaN from pandas <span class="num">shift(-1)</span> was counted as a change, skewing the Pareto-frontier ranking.</li>
<li>Fixed the count and guarded a KeyError in ParetoFrontier the fix made reachable; added unit tests and corrected four end-to-end expectations — <span class="num">+122 / −8</span> across 5 files.</li>
</ul>
<div class="chips">
<span class="chip">Python</span><span class="chip">pandas</span><span class="chip">Pytest</span>
</div>
</article>
<article class="card reveal">
<div class="card-top">
<h3><a href="https://github.com/keras-team/keras/pull/23639" target="_blank" rel="noopener">Keras <span class="ext">↗</span></a></h3>
<span class="tag-flag">In review</span>
</div>
<p class="meta">Deep-learning framework · PR #23639</p>
<ul>
<li>Fixed silent int→float truncation in selu, soft_shrink and sparse_plus — selu was degrading to elu on integer inputs — across the NumPy, TensorFlow, PyTorch and OpenVINO backends and the symbolic output spec.</li>
<li>Added tests covering the corrected behavior on every backend.</li>
</ul>
<div class="chips">
<span class="chip">Python</span><span class="chip">TensorFlow</span><span class="chip">PyTorch</span>
</div>
</article>
</div>
</div>
</section>
<section id="research" class="section-alt">
<div class="wrap">
<div class="sec-head reveal">
<span class="eyebrow">Research</span>
<h2>Applied ML research <span class="count">Aug 2025 — present</span></h2>
</div>
<div class="research reveal">
<div>
<h3>AI / Machine Learning Research Assistant</h3>
<p class="meta">iResearchE³ Lab</p>
<p class="meta">University of Texas at Arlington</p>
<div class="chips">
<span class="chip">scikit-learn</span><span class="chip">XGBoost</span><span class="chip">TensorFlow</span><span class="chip">Keras</span><span class="chip">Optuna</span><span class="chip">SHAP</span>
</div>
</div>
<ul>
<li>Built the lab's end-to-end ML pipeline in Python to predict rock permeability from NMR T2 measurements across <span class="num">720</span> heterogeneous carbonate core samples, handling targets spanning six orders of magnitude with log-space modelling and CDF-based feature encoding.</li>
<li>Tuned XGBoost, Random Forest and deep neural-network regressors with Optuna (TPE) under repeated 5-fold × 2 cross-validation across three nested feature scenarios, improving test R² by <span class="num">~50–60%</span> over the lab's SDR physics baseline (<span class="num">R² ≈ 0.42</span> RF/NN, 0.40 XGBoost vs. 0.26 SDR).</li>
<li>Stress-tested robustness across seven training sizes (120–720 samples) and 50 repeated 80/20 splits to map how accuracy scales with data volume.</li>
<li>Used SHAP and Spearman rank correlation to show that 4 of 11 features drive roughly <span class="num">95%</span> of the output. Co-authoring the manuscript.</li>
</ul>
</div>
</div>
</section>
<section id="skills">
<div class="wrap">
<div class="sec-head reveal">
<span class="eyebrow">Toolkit</span>
<h2>What I work with</h2>
</div>
<div class="skillgrid">
<div class="skillcard reveal">
<h3>AI & LLM engineering</h3>
<div class="tags">
<span>LangGraph</span><span>LangChain</span><span>RAG (hybrid & vector)</span><span>Prompt engineering</span><span>Function calling</span><span>Structured outputs</span><span>LLM evaluation</span><span>Multi-provider routing</span><span>Grounding & guardrails</span><span>STT / TTS</span><span>LangSmith</span><span>Claude</span><span>OpenAI</span><span>Gemini</span><span>Groq</span><span>Llama 3.3</span>
</div>
</div>
<div class="skillcard reveal d1">
<h3>Machine learning</h3>
<div class="tags">
<span>scikit-learn</span><span>XGBoost</span><span>Random Forest</span><span>TensorFlow</span><span>Keras</span><span>PyTorch</span><span>CNN</span><span>LSTM</span><span>Optuna</span><span>SHAP</span><span>Cross-validation</span><span>Hyperparameter tuning</span><span>Model interpretability</span><span>NumPy</span><span>pandas</span>
</div>
</div>
<div class="skillcard reveal">
<h3>Languages</h3>
<div class="tags">
<span>Python</span><span>TypeScript</span><span>JavaScript</span><span>Java</span><span>C++</span><span>C</span><span>SQL</span>
</div>
</div>
<div class="skillcard reveal d1">
<h3>Frameworks & web</h3>
<div class="tags">
<span>Next.js</span><span>React</span><span>FastAPI</span><span>Node.js</span><span>Express</span><span>REST</span><span>GraphQL</span>
</div>
</div>
<div class="skillcard reveal">
<h3>Data & cloud</h3>
<div class="tags">
<span>PostgreSQL</span><span>pgvector (HNSW)</span><span>Supabase</span><span>Vector embeddings</span><span>Docker</span><span>Vercel</span><span>Hugging Face Spaces</span><span>Git</span><span>GitHub Actions</span><span>Stripe</span><span>Clerk</span>
</div>
</div>
<div class="skillcard reveal d1">
<h3>Testing & tooling</h3>
<div class="tags">
<span>Jest</span><span>Pytest</span><span>Selenium</span><span>Claude Code</span><span>Cursor</span><span>GitHub Copilot</span>
</div>
</div>
</div>
</div>
</section>
<section id="background" class="section-alt">
<div class="wrap">
<div class="sec-head reveal">
<span class="eyebrow">Background</span>
<h2>Education & publication</h2>
</div>
<div class="two">
<div class="panel reveal">
<div class="edu">
<h3>M.S. Computer Science</h3>
<p class="meta">University of Texas at Arlington — Arlington, TX</p>
<p class="meta">Dual specialization — Software Engineering & Intelligent Systems (AI/ML)</p>
<p class="meta mono">Aug 2024 – May 2026 · GPA 3.60 / 4.00</p>
</div>
<div class="edu">
<h3>B.Tech Information Technology</h3>
<p class="meta">Rajalakshmi Engineering College — Chennai, India</p>
<p class="meta mono">Aug 2020 – May 2024 · CGPA 8.28 / 10.00, First Class</p>
</div>
</div>
<div class="panel pub reveal d1">
<h3>Publication <span class="ieee">IEEE</span></h3>
<p>Sign Language Caption Generation Using LSTM — IEEE International Conference for Convergence in Technology (I2CT), 2024. Real-time sign-language captioning from MediaPipe Holistic keypoints, decoded by an LSTM action-detection model.</p>
<p><a href="https://ieeexplore.ieee.org/document/10543890" target="_blank" rel="noopener">Read on IEEE Xplore →</a></p>
</div>
</div>
</div>
</section>
<footer id="contact">
<div class="wrap">
<span class="eyebrow">Contact</span>
<h2>Let's talk about what you're building</h2>
<p>Open to AI/ML and LLM engineering roles, and happy to relocate. The fastest way to reach me is email.</p>
<div class="contact">
<a class="primary" href="mailto:reachnanduu24@gmail.com">reachnanduu24@gmail.com</a>
<a href="Nantha_Kumar_Resume.pdf" download>Download résumé</a>
<a href="tel:8178197132">817-819-7132</a>
<a href="https://www.linkedin.com/in/nanthakumarashokanand" target="_blank" rel="noopener">LinkedIn</a>
<a href="https://github.com/Nanduu24" target="_blank" rel="noopener">GitHub</a>
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<span>Nantha Kumar · AI Engineer</span>
<span>Irving, Texas</span>
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