portfolio/index.html at main · ankit282k/portfolio · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Ankit Saxena — Portfolio</title>

<link rel="preconnect" href="https://fonts.googleapis.com">
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap" rel="stylesheet">

<style>
:root {
  /* Dark Grey & Obsidian Palette */
  --bg: #0f1115; /* Deep slate/black */
  --bg-gradient: radial-gradient(circle at top left, #1e293b, #0f1115, #020617);
  --glass: rgba(30, 41, 59, 0.5); /* Semi-transparent charcoal */
  --border: rgba(255, 255, 255, 0.08);
  --accent: linear-gradient(135deg, #3b82f6, #8b5cf6); /* Electric blue to purple */
  --text-primary: #f8fafc;
  --text-muted: #94a3b8;
  --glow: rgba(59, 130, 246, 0.15);
}

/* Reset & Global */
* { box-sizing: border-box; margin: 0; padding: 0; }

body {
  font-family: 'Inter', sans-serif;
  background: var(--bg);
  background-image: var(--bg-gradient);
  color: var(--text-primary);
  line-height: 1.6;
  scroll-behavior: smooth;
  overflow-x: hidden;
}

/* Smooth Entrance Animation */
section, header {
  animation: fadeUp 1s cubic-bezier(0.2, 0.8, 0.2, 1) forwards;
  opacity: 0;
}
@keyframes fadeUp {
  from { transform: translateY(30px); opacity: 0; }
  to { transform: translateY(0); opacity: 1; }
}

/* Floating Nav - Modern Pill */
nav {
  position: fixed;
  top: 24px;
  left: 50%;
  transform: translateX(-50%);
  background: rgba(15, 23, 42, 0.8);
  backdrop-filter: blur(12px);
  -webkit-backdrop-filter: blur(12px);
  border: 1px solid var(--border);
  padding: 10px 20px;
  border-radius: 100px;
  z-index: 1000;
  display: flex;
  gap: 10px;
}
nav a {
  padding: 6px 16px;
  font-size: 14px;
  font-weight: 500;
  color: var(--text-muted);
  text-decoration: none;
  transition: 0.3s;
  border-radius: 50px;
}
nav a:hover {
  color: #fff;
  background: rgba(255,255,255,0.05);
}

/* Hero Section */
header {
  text-align: center;
  padding: 180px 20px 80px;
}
.hero-img {
  width: 150px;
  height: 150px;
  border-radius: 40px; /* Modern squircle */
  object-fit: cover;
  border: 1px solid var(--border);
  box-shadow: 0 0 40px var(--glow);
  margin-bottom: 20px;
}
h1 {
  font-size: clamp(40px, 8vw, 72px);
  font-weight: 800;
  letter-spacing: -2px;
  background: var(--accent);
  -webkit-background-clip: text;
  -webkit-text-fill-color: transparent;
}
.hero-sub {
  font-size: 20px;
  color: var(--text-muted);
  font-weight: 400;
}

/* Sections */
section {
  max-width: 1000px;
  margin: auto;
  padding: 80px 24px;
}
h2 {
  font-size: 32px;
  margin-bottom: 30px;
  font-weight: 700;
  display: flex;
  align-items: center;
  gap: 15px;
}
h2::after {
  content: "";
  height: 1px;
  flex: 1;
  background: var(--border);
}

/* Modern Glass Card */
.card {
  background: var(--glass);
  backdrop-filter: blur(20px);
  -webkit-backdrop-filter: blur(20px);
  border: 1px solid var(--border);
  border-radius: 24px;
  padding: 30px;
  transition: all 0.4s cubic-bezier(0.175, 0.885, 0.32, 1.275);
}
.card:hover {
  transform: translateY(-8px) scale(1.01);
  border-color: rgba(255,255,255,0.2);
  box-shadow: 0 20px 40px rgba(0,0,0,0.4);
}

/* Skill Pills */
.skills-grid {
  display: grid;
  grid-template-columns: repeat(auto-fit, minmax(160px, 1fr));
  gap: 15px;
}
.skills-grid .card {
  padding: 15px;
  text-align: center;
  font-size: 14px;
  font-weight: 600;
  background: rgba(255,255,255,0.03);
}

/* Project & Experience Content */
.grid { display: grid; gap: 24px; }
p { color: var(--text-muted); margin-bottom: 15px; }
strong { color: #fff; font-size: 1.1rem; }
ul { list-style: none; margin-top: 15px; }
ul li {
  position: relative;
  padding-left: 20px;
  margin-bottom: 8px;
  color: var(--text-muted);
}
ul li::before {
  content: "→";
  position: absolute;
  left: 0;
  color: #3b82f6;
}

.social-links {
  display: flex;
  gap: 20px;
  margin-top: 12px;
}

.social-links a img {
  width: 36px;
  height: 36px;
  transition: transform 0.2s ease, opacity 0.2s ease;
  cursor: pointer;
}

.social-links a img:hover {
  transform: scale(1.1);
  opacity: 0.85;
}

/* Links */
a { color: #3b82f6; transition: 0.3s; }
a:hover { color: #8b5cf6; }

footer {
  padding: 100px 20px 40px;
  text-align: center;
  color: #475569;
  font-size: 14px;
}
</style>
</head>

<body>

<nav>
  <a href="#about">About</a>
  <a href="#skills">Skills</a>
  <a href="#projects">Projects</a>
  <a href="#experience">Experience</a>
  <a href="#Certifications">Certifications</a>
  <a href="assets/Resume.pdf" download="Ankit_Saxena_Resume.pdf">Resume</a>
  <a href="#contact">Contact</a>
</nav>

<header>
  <img src="profile.jpeg" class="hero-img" alt="Ankit Saxena" />
  <h1>ANKIT SAXENA</h1>
  <p class="hero-sub">Data Engineer • GenAI Engineer </p>
</header>

<section id="about">
<h2>About Me</h2>
<div class="card">
<p>I’m a AI and Machine Learning Engineer focused on building reliable, production-ready systems. I enjoy working on problems where data quality, modeling decisions, and system design matter as much as raw performance.</p>
<p>I take a pragmatic, production-first approach—thinking beyond notebooks to how solutions behave in the real world. I value clarity, thoughtful trade-offs, and building systems that are stable, explainable, and built to last.</p>
</div>
</section>

<section id="skills">
<h2>Skills & Technologies</h2>
<div class="grid skills-grid">
<div class="card">Python</div>
<div class="card">SQL</div>
<div class="card">ETL</div>
<div class="card">Databricks</div>
<div class="card">Machine Learning</div>
<div class="card">Apache Spark</div>
<div class="card">AWS</div>
<div class="card">Docker</div>
<div class="card">Snowflake / Redshift</div>
<div class="card">GitHub Actions</div>
<div class="card">MLOps</div>
<div class="card">LLMs & Generative AI</div>
<div class="card">RAG Pipelines</div>
<div class="card">LangChain & LlamaIndex</div>
<div class="card">Hugging Face</div>
</div>
</section>

<section id="projects">
<h2>Featured Projects</h2>
<div class="grid projects-grid">

<div class="card">
<strong>Retail SKU Demand Prediction</strong>
<ul>
<li>End-to-end demand forecasting system to predict SKU-level daily demand for a retail store over a 21-day future horizon.</li>
<li> The solution uses classical machine learning / statistical techniques, applies standard time-series feature engineering, and evaluates performance using RMSE.</li>
</ul>
</div>

<div class="card">
<strong>Brain Box</strong>
<ul>
<p>Brain Box is a Retrieval-Augmented Generation (RAG) chat bot.
The backend ingests documents, builds a vector store, and serves a chat API that answers questions using retrieved context</p>
<li>Vector embedding and storage using ChromaDB</li>
<li>Semantic retrieval and prompt orchestration with LangChain</li>
<li>It uses Models powered by Azure AI Foundry</li>
<li>Document ingestion and vector store rebuild on upload</li>
<li>Pluggable LLM / embedding configuration via environment variables</li>
</ul>
</div>

<div class="card">
<strong>Real-Time Time Streaming ETL Pipeline</strong>
<ul>
<p>A real-time ETL pipeline designed to ingest, process, and transform streaming data with low latency, enabling timely analytics and downstream machine learning use cases</p>
<li>Built a scalable streaming pipeline to ingest and process events in near real time, ensuring data consistency and fault tolerance across the ETL flow.</li>
<li>	Implemented real-time transformations and aggregations with monitoring and error handling to support reliable downstream analytics and ML systems</li>
</ul>
</div>

</div>
</section>

<section id="experience">
<h2>Experience</h2>
<div class="grid experience-list">

<div class="card">
  <strong>AI/ML Engineer — Simplifix Systems (Vijay Sales)</strong>
  <p><em>Jan 2026 – Present</em></p>
  <ul>
    <li>Built and maintained scalable data pipelines for retail transaction and inventory data using Databricks and AWS.</li>
   <li>Developed machine learning models for inventory prediction, improving demand forecasting and stock planning across multiple branches.</li>
   <li>Leveraged LLMs and AI-based approaches to analyze retail data and generate business insights for operational decision-making.</li>
   <li>Designed and optimized data engineering workflows including ETL/ELT pipelines, data transformations, and data quality checks.</li>
   <li>Created interactive dashboards and analytics reports for business stakeholders to track sales performance, inventory levels, and operational KPIs.</li>
  </ul>
</div>

<div class="card">
  <strong>Associate Software Developer — Bosch Global Software Technologies</strong>
  <p><em>Feb 2025 – Jan 2026</em></p>
  <ul>
    <li>Developed and deployed <strong>LLM-based microservices</strong> and data-driven automation solutions, improving team productivity by <strong>32%</strong>.</li>
    <li>Built scalable <strong>NLP and embedding pipelines</strong> using PyTorch, HuggingFace, and TensorFlow, reducing inference latency by <strong>28%</strong>.</li>
    <li>Designed reusable AI components enabling a <strong>multi-use architecture</strong>, reducing development time for new use cases by <strong>40%</strong>.</li>
    <li>Implemented production-grade applications using <strong>Python, Spark, FastAPI, and Docker</strong>, with monitoring for model drift and performance regression.</li>
    <li>Collaborated in <strong>Agile / SAFe</strong> environments to translate requirements into reliable AI and data solutions.</li>
  </ul>
</div>

<div class="card">
  <strong>Data Engineer Trainee — FranConnect Pvt. Ltd.</strong>
  <p><em>Sep 2024 – Feb 2025</em></p>
  <ul>
    <li>Engineered scalable <strong>ETL and feature pipelines</strong> using PySpark, Hive, and SQL, improving processing performance by <strong>45%</strong>.</li>
    <li>Optimized ingestion of <strong>30M+ records</strong> across distributed systems, ensuring data quality, governance, and lineage.</li>
    <li>Reduced Spark job costs by <strong>22%</strong> through partitioning, caching, and schema evolution strategies.</li>
    <li>Automated data and ML pipeline deployments using <strong>Docker and GitHub Actions</strong>, reducing deployment failures by <strong>80%</strong>.</li>
  </ul>
</div>

<div class="card">
  <strong>Data Scientist Trainee — Dyno India Pvt. Ltd.</strong>
  <p><em>Feb 2023 – Aug 2023</em></p>
  <ul>
    <li>Built and evaluated ML models (Logistic Regression, Random Forest, XGBoost, SVM, K-Means, PCA, ARIMA) achieving <strong>84–92%</strong> accuracy.</li>
    <li>Developed deep learning models (<strong>CNN, LSTM</strong>) for text and image data, reducing validation loss by <strong>30%</strong>.</li>
    <li>Implemented recommender systems using <strong>Collaborative Filtering and FISM</strong>, improving precision by <strong>25%</strong>.</li>
  </ul>
</div>

</div>
</section>

<section id="Certifications">
<h2>Certifications</h2>
<div class="card">
<p><strong>• AWS Certified Cloud Practitioner</p>
</div>
</section>

<section id="contact">
  <h2>Contact</h2>
  <div class="card contact-icons">

    <p>Email:
      <a href="mailto:ankit282k@gmail.com">ankit282k@gmail.com</a>
    </p>
    <p>Phone:
      <a href="tel:+917500335301">+91 7500335301</a>
    </p>
    </div>
  </div>
</section>

<section id="Socials">
  <h2>Socials</h2>
  <div class="card contact-icons">

    <div class="social-links">
      <a href="https://linkedin.com/in/ankit282k" target="_blank" aria-label="LinkedIn">
        <img src="assets/linkedin.png" alt="LinkedIn" />
      </a>

      <a href="https://github.com/ankit282k" target="_blank" aria-label="GitHub">
        <img src="assets/github.png" alt="GitHub" />
      </a>
    </div>

  </div>
</section>

<footer>© 2026 Ankit Saxena</footer>

</body>
</html>