Naraen Palanikumar

Bioinformatics AI Engineer & Cloud Architect. Building scalable machine learning systems and operating in stealth.

About

I focus on building production-ready genomic analysis platforms and applied AI for biological data. My work spans single-cell sequencing, high-throughput transcriptomics, and cloud-native ML systems—with publications in peer-reviewed journals and hands-on experience with GCP, Vertex AI, and LLM-augmented workflows.

Explore my portfolio, view my CV, or get in contact if you're interested in collaborating.

Projects

Stealth Genomics AI Platform

Python · GCP · Vertex AI · RAG · Docker

Production-oriented SaaS for genomic analysis: FASTA/FASTQ/VCF and 10x Genomics ingestion, k-mer feature engineering, and RNA-seq differential expression. Integrates Google Vertex AI with a tool-augmented RAG loop and function calling to cross-reference variants with PubMed and GWAS Catalog. Containerized and deployed on Cloud Run with multi-tenant IAM and artifact registry.

Stealth AI Media SaaS

Next.js · Express · ComfyUI · GCP Compute

Full-stack app for on-demand AI image and video generation. Wake-on-demand GCP VM lifecycle state machine runs ComfyUI/SDXL on A100 spot instances only when generation requests are active, with template-based JSON workflows, dynamic prompt logic, and automated history and metadata tracking.

Experience

Independent Bioinformatics Consultant

Remote · July 2025 – Present

Consulting on single-cell sequencing pipelines and metabolic pathway analysis; architecting cloud-native data infrastructure and applied AI/ML workflows for research teams.
Bioinformatics Research Assistant, Malloy Lab

USUHS · Bethesda, MD · Oct 2024 – Jun 2025

Led bioinformatic analysis of CD4 T-cell repertoires contributing to a publication in Frontiers in Immunology; managed large-scale transcriptomic datasets and optimized processing for scRNA-seq and flow cytometry.
Undergraduate Research Assistant, Sweeney Lab

UIUC · Champaign, IL · Aug 2021 – Jun 2023

Analyzed scRNA-seq with Cell Ranger and Seurat; developed an interactive R Shiny app for fiber photometry visualization; co-authored a publication in The Journal of Neuroscience.