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    content="Arunkumar Kesavan - MLOps & AI Platform Engineer specializing in AI Platform Engineering, MLOps, LLMOps, GenAI, Agentic AI, RAG, Kubernetes, Cloud Infrastructure, and Distributed Systems."
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  <title>Arunkumar Kesavan | MLOps & AI Platform Engineer</title>

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          <h1>Arunkumar Kesavan</h1>

          <p class="role">
            MLOps &amp; AI Platform Engineer
          </p>
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        <div class="header-label">
          AI PLATFORM ENGINEERING<br>
          MLOps • LLMOps • GenAI
        </div>

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      <div class="contact">

        <span>Chennai, Tamil Nadu, India</span>
        <span>+91-9443862476</span>

        <a href="mailto:arunkumar.rk7@gmail.com">
          arunkumar.rk7@gmail.com
        </a>

        <a
          href="https://www.linkedin.com/in/arunkumar-kesavan-58104a416"
          target="_blank"
          rel="noopener noreferrer"
        >
          LinkedIn
        </a>

        <a
          href="https://ak-rk7.github.io"
          target="_blank"
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        >
          Portfolio
        </a>

        <a
          href="https://github.com/ak-rk7"
          target="_blank"
          rel="noopener noreferrer"
        >
          GitHub
        </a>

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        <h2>Professional Summary</h2>

        <p>
          I’m an <strong>AI Platform and MLOps Engineer</strong> who has grown
          from a strong <strong>DevOps and infrastructure foundation</strong>
          into building production-ready AI and ML platforms.

          My approach is <strong>bottom-up: infrastructure → cloud-native
          platforms → MLOps → LLMOps → production AI systems</strong>. This
          foundation helps me design AI platforms that are not only intelligent,
          but also scalable, reliable, observable, secure, and operationally
          efficient.

          Currently, I work on <strong>enterprise AI platform
          engineering</strong>, building and operating production-grade systems
          for <strong>Generative AI, agentic workflows, RAG, ML pipelines, and
          cloud-native infrastructure</strong>.

          My experience includes designing enterprise AI platforms,
          multi-agent architectures, Graph RAG systems, hybrid retrieval
          pipelines, data workflows, Kubernetes platforms, distributed storage,
          event-driven systems, and production observability.

          Earlier in my career, I focused on <strong>DevOps, AWS
          infrastructure, application deployment, databases, automation,
          security, and cloud platform operations</strong>.

          This progression from infrastructure engineering to AI platform
          engineering gives me a practical understanding of the complete
          lifecycle—from provisioning infrastructure and deploying workloads
          to operating ML and GenAI systems in production.

          I’m particularly interested in building the engineering foundations
          that enable AI teams to move from experimentation to reliable
          production systems, with an emphasis on <strong>platform engineering,
          MLOps, LLMOps, automation, scalability, observability, and AI
          infrastructure</strong>.
        </p>

        <p>
          <strong>Core areas of expertise:</strong>
          AI Platform Engineering | MLOps | LLMOps | GenAI | Agentic AI | RAG |
          Graph RAG | Kubernetes | Cloud Infrastructure | ML Pipelines |
          Distributed Systems | Platform Engineering | Observability | Automation
        </p>

        <p>
          I enjoy solving complex infrastructure and platform challenges and
          turning them into <strong>reliable, scalable systems</strong> that
          enable teams to build and operate AI applications efficiently.
        </p>

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        <h2>Technical Skills</h2>

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            <h3>GenAI &amp; LLMOps</h3>
            <p>
              Graph RAG, LangGraph, Multi-Agent Systems, Pinecone, Neo4j,
              Elasticsearch, Hybrid Retrieval, HyDE, Cross Encoder Reranking,
              Prompt Engineering
            </p>
          </div>

          <div class="skill">
            <h3>MLOps &amp; Data</h3>
            <p>
              MLflow, Airflow, Kafka, Celery, Redis, Snowflake, ETL Pipelines
            </p>
          </div>

          <div class="skill">
            <h3>Cloud Native</h3>
            <p>
              Kubernetes, MicroK8s, Docker, Terraform, AWS EC2, S3, RDS,
              IAM, Jenkins, NGINX
            </p>
          </div>

          <div class="skill">
            <h3>Engineering</h3>
            <p>
              Python, FastAPI, Flask, PostgreSQL, MySQL, OpenTelemetry,
              Prometheus, Grafana
            </p>
          </div>

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        <h2>Professional Experience</h2>

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            <div>
              <h3>Software Engineer - AI Platform Engineering / MLOps & GenAI</h3>
              <p class="company">Creative Engineering</p>
            </div>

            <time>April 2024 - Present</time>

          </div>

          <ul>
            <li>
              Designed and developed the Enterprise Compliance &amp; Risk
              Intelligence Platform (ECRIP), an agentic AI platform built on
              distributed services.
            </li>

            <li>
              Built stateful multi-agent orchestration using LangGraph with 6
              specialist AI agents and human approval workflows.
            </li>

            <li>
              Implemented a hybrid RAG pipeline combining dense retrieval, BM25
              search, Reciprocal Rank Fusion (RRF), and cross-encoder reranking.
            </li>

            <li>
              Developed a Neo4j-based Graph RAG engine supporting multi-hop
              enterprise reasoning and knowledge retrieval.
            </li>

            <li>
              Implemented event-driven data and ML pipelines using Kafka and
              Airflow with KEDA-based autoscaling for Kubernetes workloads.
            </li>

            <li>
              Managed air-gapped Kubernetes environments using MicroK8s and
              Rook-Ceph for distributed storage.
            </li>

            <li>
              Implemented production observability using Datadog and the Grafana
              LGTM stack for monitoring, logging, tracing, and alerting.
            </li>
          </ul>

        </article>


        <article class="experience">

          <div class="experience-title">

            <div>
              <h3>Engineer - DevOps / Infrastructure Analyst</h3>
              <p class="company">HTC Global Services INC</p>
            </div>

            <time>August 2022 - March 2024</time>

          </div>

          <ul>
            <li>
              Supported cloud-native infrastructure and applications for a
              Financial Data Analytics and Insights Platform.
            </li>

            <li>
              Productionized enterprise applications through automated
              deployment and infrastructure workflows.
            </li>

            <li>
              Developed an MVP for ML-based fraud detection in online insurance
              claims, recognized among the Top 10 ideas at the AIML Ideathon
              2023.
            </li>

            <li>
              Built deployment automation pipelines that reduced application
              deployment time by 30%.
            </li>
          </ul>

        </article>


        <article class="experience">

          <div class="experience-title">

            <div>
              <h3>AWS DevOps Support Engineer</h3>
              <p class="company">NB Media Tech Pvt Ltd</p>
            </div>

            <time>January 2022 - July 2022</time>

          </div>

          <ul>
            <li>
              Managed AWS-hosted applications across EC2, Lightsail, and
              CloudFront environments.
            </li>

            <li>
              Performed MySQL and PostgreSQL database optimization and
              performance tuning.
            </li>

            <li>
              Implemented HTTPS termination, AWS IAM policies, and JWT-based
              security middleware.
            </li>
          </ul>

        </article>

      </section>


      <!-- Featured Projects -->
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        <h2>Featured Projects</h2>


        <article class="project">

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            <h3>
              <a
                href="https://github.com/AK-RK7/ml-sys-book"
                target="_blank"
                rel="noopener noreferrer"
              >
                ML Systems Study Assistant - Hybrid RAG &amp; Evaluation
              </a>
            </h3>

            <p class="tech">
              Python | RAG | Hybrid Retrieval | BM25 |
              Sentence Transformers | Flask
            </p>

          </div>

          <ul>
            <li>
              Built a Retrieval-Augmented Generation (RAG) study assistant for
              Machine Learning Systems content, implementing an end-to-end
              pipeline from document ingestion and processing to retrieval,
              generation, and evaluation.
            </li>

            <li>
              Implemented hybrid retrieval combining semantic similarity and
              BM25 keyword search with weighted score fusion to improve
              retrieval relevance across technical textbook content.
            </li>

            <li>
              Developed retrieval evaluation using Hit Rate and Mean Reciprocal
              Rank (MRR), achieving approximately 83% Hit Rate and 0.69 MRR on
              the project retrieval benchmark.
            </li>

            <li>
              Implemented LLM-as-a-Judge evaluation for assessing generated
              answer relevance and designed REST API, CLI, logging, and
              monitoring foundations for production-oriented LLM applications.
            </li>
          </ul>

        </article>


        <article class="project">

          <div class="project-title">

            <h3>
              Enterprise GenAI Compliance Intelligence Platform
            </h3>

            <p class="tech">
              LangGraph | Graph RAG | Pinecone | Neo4j | Kubernetes
            </p>

          </div>

          <ul>
            <li>
              Designed an enterprise RegTech AI platform for automated
              compliance management, regulatory gap analysis, and enterprise
              risk intelligence.
            </li>

            <li>
              Engineered a typed compliance knowledge graph enabling multi-hop
              reasoning across regulations, obligations, controls, assets, and
              risks using Neo4j Graph RAG architecture.
            </li>

            <li>
              Built a hybrid retrieval pipeline combining dense vector search
              (Pinecone) and sparse keyword retrieval (Elasticsearch BM25),
              achieving 91% retrieval precision through Reciprocal Rank Fusion
              and Cross-Encoder reranking.
            </li>

            <li>
              Developed a 6-agent stateful orchestration framework using
              LangGraph with human-in-the-loop approval gates and adaptive
              instruction codebooks to improve reliability and reduce LLM
              hallucinations.
            </li>
          </ul>

        </article>


        <article class="project">

          <div class="project-title">

            <h3>
              Cloud-Native Financial Data Engineering &amp; Analytics Platform
            </h3>

            <p class="tech">
              Django | PostgreSQL | Snowflake | React.js | Power BI
            </p>

          </div>

          <ul>
            <li>
              Developed a financial analytics platform integrating data from
              APIs, CSV sources, and enterprise databases for centralized
              reporting and decision intelligence.
            </li>

            <li>
              Implemented data cleaning, transformation, statistical analysis,
              and trend forecasting workflows for financial performance
              analysis.
            </li>

            <li>
              Designed application architecture using React.js frontend,
              Django backend services, PostgreSQL transactional storage, and
              Snowflake analytical warehouse infrastructure.
            </li>

            <li>
              Integrated Power BI dashboards and reporting workflows to provide
              interactive business intelligence and operational visibility.
            </li>
          </ul>

        </article>


        <article class="project">

          <div class="project-title">

            <h3>
              Containerized Enterprise Compliance Automation Platform
            </h3>

            <p class="tech">
              Docker | Flask | Angular | MySQL | AWS S3
            </p>

          </div>

          <ul>
            <li>
              Built a regulatory compliance management platform supporting
              automated compliance checks, regulatory tracking, reporting, and
              alerts.
            </li>

            <li>
              Implemented document management workflows with AWS S3 storage for
              secure compliance document ingestion and categorization.
            </li>

            <li>
              Developed containerized application architecture using Angular
              frontend, Flask backend services, and MySQL database operations.
            </li>

            <li>
              Standardized deployment environments using Docker-based packaging
              for consistent application delivery.
            </li>
          </ul>

        </article>


        <article class="project">

          <div class="project-title">

            <h3>
              Enterprise Financial Operations Platform
            </h3>

            <p class="tech">
              React.js | Django | PostgreSQL | AWS
            </p>

          </div>

          <ul>
            <li>
              Developed a centralized accounting platform managing client
              profiles, financial transactions, reporting workflows, and audit
              records.
            </li>

            <li>
              Implemented compliance-focused audit trails tracking transaction
              and client data modifications to support governance requirements
              including GDPR and SOX.
            </li>

            <li>
              Designed full-stack architecture using React.js frontend, Django
              backend APIs, PostgreSQL database services, and AWS-hosted
              deployment infrastructure.
            </li>
          </ul>

        </article>

      </section>


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        <h2>Education</h2>

        <div class="education">

          <div>
            <strong>B.E. Computer Science &amp; Engineering</strong>
            <span>60%</span>
          </div>

          <div>
            <strong>Higher Secondary Education (Class XII)</strong>
            <span>77%</span>
          </div>

          <div>
            <strong>Secondary School Leaving Certificate (Class X)</strong>
            <span>77%</span>
          </div>

        </div>

      </section>


      <!-- Certifications -->
      <section>

        <h2>Certifications</h2>

        <ul class="certifications">

          <li>
            100 Hours Industrial Training Program on Cyber Security &amp; Ethical
            Hacking -- NIELIT (2024)
          </li>

          <li>
            Completed Azure &amp; Power Platform Training -- HTC (2023)
          </li>

          <li>
            Ranked #7 in the Top 10 Ideas -- Ideathon, HTC (2023)
          </li>

          <li>
            Tosca AS1 &amp; AS2 Certification -- Tricentis (2022)
          </li>

          <li>
            Python Fundamentals Certification -- Great Learning (2021)
          </li>

        </ul>

      </section>

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      © 2026 Arunkumar K | MLOps Engineer | AI Platform Engineer
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