Hemapriya Senthilkumar

Software Engineer

Bengaluru, Karnataka, India5 yrs 10 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Designed an AI debugger reducing manual effort by 40 minutes.
Migrated Docker projects to Kubernetes, cutting deployment time by 60%.
Developed a dashboard improving release readiness visibility by 60%.

Stackforce AI infers this person is a Backend-heavy Software Engineer specializing in AI and Automation.

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Skills

Core Skills

KubernetesMicroservicesAgentic AiInfrastructure ManagementData VisualizationAutomationCi/cdSoftware DevelopmentData Processing

Other Skills

DenodoDockerPower BITerraformPythonAuto-GenProxmoxPackerJenkinsPywin32MongoDBC#ReactJSGenerative AIAgentic AI Development

About

Passionate and solutions-driven Senior Python Developer with a strong foundation in the end-to-end software development lifecycle, designing scalable distributed systems, and Agentic AI. I thrive at the intersection of software engineering and AI innovation. Expertise in: Automation, including Bash scripting, CI/CD pipelines, and process optimization Web-based backend solutions following microservices architecture Scalable system design and backend development Containerized deployments using Docker & Kubernetes Agentic AI systems and feedback-aware LLM architectures Dashboard-based insights and visualization tools Writing clean, test-driven code using TDD principles Let’s connect if you’re into building smart systems, debugging complex issues, or just geeking out about Python, backend solutions, or AI systems!

Experience

5 yrs 10 mos

Total Experience

2 yrs 11 mos

Average Tenure

4 yrs 10 mos

Current Experience

Intel corporation

3 roles

Software Developer II

Promoted

Mar 2024 – Present · 2 yrs 2 mos

Autonomous AI System Debug Agent: Designed and built the end-to-end development of a multi-agent AI debugger using the Auto-Gen library to identify failing system components, cutting manual effort by 40 minutes per failure while achieving 72% accuracy.
Kubernetes Migration for Distributed System: Owned and implemented the migration of a Docker-based distributed project to a Kubernetes + Helm-based deployment. This reduced deployment time from half a day to 20 minutes, enhancing developer productivity and system reliability.
On-Prem Coder Space Setup: Set up a cost-effective Coder workspace using Proxmox, Terraform, and Packer to replace AWS VMs with on-prem VMs, reducing infrastructure costs by 50–70% and improving resource control and scalability.
Release Readiness Dashboard for Intel Tiber Edge Platform (ITEP): Led the modelling and development of a Power BI dashboard to track release readiness metrics, improving visibility across multiple teams and reducing manual tracking efforts by 60%. Enabled data-driven decision-making and accelerated release planning.

MicroservicesDenodoKubernetesDockerPower BITerraform

Software Developer I

Jun 2021 – Feb 2024 · 2 yrs 8 mos

Manifest-Based Release Automation for Ledge Park: Automated release deployment using manifest-based version control, reducing manual effort by 80% and accelerating release candidate selection. Streamlined CI/CD pipelines with Jenkins and Docker, enabling nightly builds and validation tests for efficient multi-environment deployments.
MS Office Automation for IFPD Use Cases: Built automation using Pywin32 to simulate workloads across Excel, Word, and PowerPoint for stress testing Interactive Flat Panel Display (IFPD) scenarios—boosting productivity and test coverage.
PXE-Based Firmware & OS Provisioning System(SPARKS): Contributed to a PXE-based distributed provisioning system (microservice) reducing firmware and OS flashing time for Intel platforms by 30 minutes per instance across Linux and Windows systems.
Automated OS Installations: Created OS flashing profiles for Ubuntu, Windows, Fedora, CentOS, and Red Hat in both disk image and ISO formats (auto-install, kickstart) which enables users to save ~1 hour per installation.
Scalable Test Automation Framework(STAR): Automated the end-to-end release of STAR framework libraries as pip packages within Intel and contributed by implementing test cases for multiple platform validation use cases such as OS flashing, IFWI flashing, and power management, hosting on Cloud Foundry. Enabled validation teams to automate 100% of their test pipelines, eliminating manual efforts.

DockerAutomationJenkinsPywin32

Graduate Technical Intern

Aug 2020 – May 2021 · 9 mos

Developed a PDF parser to automate the extraction of text, images, and tables from PDFs using Python & MongoDB, retaining the original hierarchical structure of the data.
Automated License Installation by using Outlook email as triggering event. Saved up to 20mins per installation. Built using C#, Python for CLI/Backend Server and UI as well using ReactJS.

PythonMongoDBC#ReactJSSoftware Development