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    <title>Aydin Aghaliyev</title>
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      <title>About</title>
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      <pubDate>Mon, 01 Jan 2024 08:00:00 +0000</pubDate><author>aydin.aghaliyev@gmail.com (Aydin Aghaliyev)</author>
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      <description>&lt;h2 id=&#34;about-me&#34;&gt;About Me&lt;/h2&gt;&#xA;&lt;p&gt;I&amp;rsquo;m &lt;strong&gt;Aydin Aghaliyev&lt;/strong&gt;, a Data Scientist &amp;amp; AI Engineer at Philips, based in Rotterdam, The Netherlands. I build agentic AI systems for root-cause analysis and contract reasoning, with experience in knowledge graphs, MCP, NLP, and cloud infrastructure.&lt;/p&gt;&#xA;&lt;h2 id=&#34;experience&#34;&gt;Experience&lt;/h2&gt;&#xA;&lt;h3 id=&#34;philips--amsterdam-netherlands&#34;&gt;Philips — Amsterdam, Netherlands&lt;/h3&gt;&#xA;&lt;p&gt;&lt;strong&gt;Data Scientist &amp;amp; AI Engineer&lt;/strong&gt; — Full-time | Sep 2025 – Present&lt;/p&gt;&#xA;&lt;ul&gt;&#xA;&lt;li&gt;Architected and built an end-to-end root-cause analysis (RCA) pipeline that ingests incident data from ServiceNow, parses log archives into RDF knowledge graphs stored in Amazon Neptune, and exposes 50+ analysis tools to an LLM agent via MCP (Model Context Protocol)&lt;/li&gt;&#xA;&lt;li&gt;Designed a graph-based investigation algorithm with multi-factor severity scoring and pivot logic, enabling the agent to autonomously narrow investigation scope across thousands of log lines per incident&lt;/li&gt;&#xA;&lt;li&gt;Built a SPARQL pre-execution static analyzer to prevent injection attacks and catch query errors before reaching the database&lt;/li&gt;&#xA;&lt;li&gt;Deployed the MCP server on AWS Bedrock AgentCore; provisioned the entire pipeline infrastructure with Terraform (Lambda, SQS, EventBridge, S3, Neptune), with GitHub Actions CI/CD and OIDC-federated authentication&lt;/li&gt;&#xA;&lt;li&gt;Created an agentic pipeline in Databricks (PySpark) for clause extraction and reasoning over medical service contracts using LLMs&lt;/li&gt;&#xA;&lt;/ul&gt;&#xA;&lt;h3 id=&#34;philips--eindhoven-netherlands&#34;&gt;Philips — Eindhoven, Netherlands&lt;/h3&gt;&#xA;&lt;p&gt;&lt;strong&gt;Generative AI Engineer&lt;/strong&gt; — Internship | Apr 2025 – Sep 2025&lt;/p&gt;</description>
      <content:encoded><![CDATA[<h2 id="about-me">About Me</h2>
<p>I&rsquo;m <strong>Aydin Aghaliyev</strong>, a Data Scientist &amp; AI Engineer at Philips, based in Rotterdam, The Netherlands. I build agentic AI systems for root-cause analysis and contract reasoning, with experience in knowledge graphs, MCP, NLP, and cloud infrastructure.</p>
<h2 id="experience">Experience</h2>
<h3 id="philips--amsterdam-netherlands">Philips — Amsterdam, Netherlands</h3>
<p><strong>Data Scientist &amp; AI Engineer</strong> — Full-time | Sep 2025 – Present</p>
<ul>
<li>Architected and built an end-to-end root-cause analysis (RCA) pipeline that ingests incident data from ServiceNow, parses log archives into RDF knowledge graphs stored in Amazon Neptune, and exposes 50+ analysis tools to an LLM agent via MCP (Model Context Protocol)</li>
<li>Designed a graph-based investigation algorithm with multi-factor severity scoring and pivot logic, enabling the agent to autonomously narrow investigation scope across thousands of log lines per incident</li>
<li>Built a SPARQL pre-execution static analyzer to prevent injection attacks and catch query errors before reaching the database</li>
<li>Deployed the MCP server on AWS Bedrock AgentCore; provisioned the entire pipeline infrastructure with Terraform (Lambda, SQS, EventBridge, S3, Neptune), with GitHub Actions CI/CD and OIDC-federated authentication</li>
<li>Created an agentic pipeline in Databricks (PySpark) for clause extraction and reasoning over medical service contracts using LLMs</li>
</ul>
<h3 id="philips--eindhoven-netherlands">Philips — Eindhoven, Netherlands</h3>
<p><strong>Generative AI Engineer</strong> — Internship | Apr 2025 – Sep 2025</p>
<ul>
<li>Built a vector-based full-text search system using Gemini embeddings, ChromaDB, FastAPI backend and Vue.js frontend; deployed with key-based authentication on Azure</li>
<li>Built a GPT-4o agentic RAG chatbot for in-company use with a full frontend, authentication system, and dynamic memory of previous conversations, deployed on Azure</li>
</ul>
<h2 id="education">Education</h2>
<p><strong>Erasmus University Rotterdam</strong> — MSc Data Science | Sep 2024 – Aug 2025
<em>Coursework: Machine learning, NLP, LLMs, Agentic AI, Web scraping, Python, R</em></p>
<p><strong>Erasmus University Rotterdam</strong> — BSc International Economics and Business Economics (IBEB) | Sep 2021 – Aug 2024
<em>Coursework: Investments, Derivatives, Statistics, Stochastic Calculus, Econometrics, Time Series Analysis, C++, R</em></p>
<h2 id="skills">Skills</h2>
<ul>
<li><strong>Programming languages:</strong> Python, TypeScript/JavaScript, C++, R, SQL</li>
<li><strong>AI &amp; ML:</strong> TensorFlow, PyTorch, LangChain, NumPy, Pandas, Pydantic, RDFLib, Agentic AI, RAG, MCP, Knowledge Graphs, SPARQL, LLM integration (AWS Bedrock, GPT-4o, Gemini)</li>
<li><strong>Backend &amp; Data:</strong> FastAPI, FastMCP, ChromaDB, Node.js, Vue.js, Spark, Databricks, Power BI, QlikSense, Excel</li>
<li><strong>Cloud &amp; Infrastructure:</strong> AWS (Lambda, SQS, S3, Neptune, Bedrock AgentCore, EventBridge), Azure, Docker, Terraform, CI/CD (GitHub Actions), ServiceNow REST API, Microsoft Office JS API</li>
</ul>
<h2 id="certifications">Certifications</h2>
<ul>
<li>GRE — 160V / 170Q <em>(ETS, Oct 2023)</em></li>
<li>IELTS — 8.0 <em>(Mar 2020)</em></li>
</ul>
<h2 id="languages">Languages</h2>
<p>Azerbaijani (native) · English (fluent) · Russian (fluent) · Turkish (fluent)</p>
<h2 id="contact">Contact</h2>
<ul>
<li>Email: <a href="mailto:aydin.aghaliyev@gmail.com">aydin.aghaliyev@gmail.com</a></li>
<li>GitHub: <a href="https://github.com/aydin-aghaliyev">aydin-aghaliyev</a></li>
<li>LinkedIn: <a href="https://linkedin.com/in/aydin-aghaliyev">aydin-aghaliyev</a></li>
</ul>
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