ANKUR.SHARMA
Selectively open to senior roles globally — Singapore (PR) · Remote · Relocation

Agentic AI & ML Engineer
building reasoning systems & foundation models
for clinical genomics.

~/ankur-sharma — zsh
❯ whoami
Ankur Sharma, PhD · Sr. Scientist/Engineer AI/ML @ Illumina · Singapore
❯ cat stack.txt
LangGraph · Claude API · PyTorch · AWS · Nextflow · Python
❯ cat focus.txt
Agentic AI · Foundation model training & fine-tuning · Clinical genomics · Drug discovery
❯ status --open-to-right-opportunities
✓Technical ownership · meaningful science · real-world stakes
✓Hard science problems · exceptional team · global reach
✓Singapore PR · Remote · Relocation-ready
❯
See projects →↓ Download CVGitHubLinkedInEmail
0+
Years experience
wet-lab → cloud ML → agentic AI
0+
WGS samples
+ 3,000 metagenomics samples
0 PB
Data managed
1 petabyte on AWS, 15 services
0+
Citations
Frontiers in Microbiology 2018
Ankur Sharma, PhD
Ankur Sharma, PhD
Sr. Scientist / Engineer AI/ML · Illumina
Open to the right opportunity
SCROLL

Work

Projects & Systems

Open-source tools, production pipelines, and research systems — spanning agentic AI, clinical ML, bioinformatics, and AI model evaluation.

Featured · Foundation Model Fine-Tuning
⭐ 2⑂ 1

genome-ft — Genomic Foundation Model Fine-Tuning

Full weight-level fine-tuning of Nucleotide Transformer v2 (50M params) on DNA classification tasks — rigorous multi-seed evaluation protocol, results published to Hugging Face Hub.

All 53.8M parameters updated (not LoRA, not frozen head) using AdamW with warmup + cosine decay and reverse-complement augmentation. Leakage-free train/val/test split, test set scored exactly once, 3 seeds with reported variance. Base model measured under identical pipeline for honest comparison.

0.872 · Promoters accuracy (MCC 0.747)0.735 · Enhancers accuracy (MCC 0.478 ± 0.003, 3 seeds)53.8M · Parameters fully fine-tuned
PyTorchHugging FaceNucleotide Transformer v2GenomicBenchmarksAdamWscikit-learn
→ GitHub →→ Hugging Face model card →
Flagship · Open Source
⭐ 3⑂ 0

agentic-genomics · GenomicsCopilot

Open-source LangGraph agent for explainable variant interpretation — every call leaves a full reasoning trace a human can audit.

VCF + HPO phenotype terms → ranked, explainable candidate variants. 7 deterministic nodes: gnomAD/ClinVar/SpliceAI lookups, ACMG-lite rule engine (PVS1 + Richards 2015), Phrank HPO scoring, LLM synthesiser, critic fact-checker. Every run emits a machine-readable reasoning trace.

7 nodes · LangGraph + critic review4 tools · MyVariant · Phrank · ACMG-lite · criticMIT · Open source, Python 3.11+
LangGraphClaude APIPydantic v2pysamStreamlitGitHub Actions
→ GitHub →→ Why agentic? →→ Architecture →→ Limitations →
Open Source · Explainable ML
⭐ 2⑂ 0

sentinel-amr — Explainable AMR Classifier

Agentic antimicrobial resistance classifier: predicts resistance, names the driving genes, detects out-of-distribution organisms, escalates uncertainty to a human.

Trained on 298 real K. pneumoniae genomes (BV-BRC AST). 5-node LangGraph agent: vectorisation → XGBoost → alignment-free k-NN novelty → SHAP TreeExplainer → critic gate. Top SHAP drivers independently rediscovered correct biology: CTX-M-15, acrAB efflux, DNA topoisomerase IV.

0.894 · ROC-AUC, group-aware holdout298 · Real BV-BRC AST genomes8 tests · Dataset · split · novelty · agent
XGBoostSHAPLangGraphBV-BRCGroupShuffleSplitMIT
→ GitHub →→ Scaling doc →
Open Source · Agentic AI
⭐ 2⑂ 0

outbreak-agent — Infectious Disease Triage

4-node LangGraph pipeline that triages infectious disease cases in under 2 seconds — built around the April 2026 MV Hondius/Andes virus event.

Self-correcting agentic pipeline: genomic_node → linkage_node → risk_node → critic_node (loops up to 3×). Generates a 3-panel matplotlib risk dashboard + structured A4 PDF triage report. No API key, no cost, 33 fully deterministic tests. Integrates with gwas_nf via gwas_bridge.py.

4 nodes · genomic · linkage · risk · critic33 tests · 23 unit + 10 integration, free to run<2 sec · CRITICAL triage, MV Hondius case
LangGraph 0.6LangChainmatplotlibReportLabpytestApache 2.0
→ GitHub →→ Blog post →
Open Source · Agent Skills
⭐ 2⑂ 0

genomics-skills — Agent-Callable Skill Library

8 pure-Python genomics skills that downstream agents can call: TCGA expression, survival analysis, protein mapping, pathway enrichment, literature search.

Agent-discoverable modules with SKILL.md contracts, CLI entrypoints, and deterministic TSV+PNG output. Pan-cancer expression uses real TCGA data (9,479 samples, 31 cancer types). LLM routing via Claude Haiku. Parquet caching for instant repeat queries.

8 skills · Agent-callable, SKILL.md contract9,479 · Real TCGA patient samplesMIT · Open source, Python 3.9+
PythonClaude HaikucBioPortal APIMyVariant.infoNCBI E-utilsPandas
→ GitHub →
Research · Production
⭐ 3⑂ 0

gwas_nf — Multi-Ethnic GWAS Pipeline

REGENIE-based Nextflow GWAS pipeline for the TEMUS multi-ethnic cohort — 4 groups, 10,000 samples, 13 phenotypes — with downstream agentic interpretation via GenomicsCopilot.

End-to-end: genotype QC → LD pruning → REGENIE Step 1+2 → multi-ethnic stratified association → Manhattan plots → HTML reports. Top hits feed GenomicsCopilot via gwas_bridge.py. Population discovery to clinical interpretation in a single command.

4 groups · TEMUS multi-ethnic cohort13 · Phenotypes, parallel GWAS runs100k · Variants, whole-genome regression
Nextflow DSL2REGENIEAWS BatchPythonR / ggplot2Docker
→ GitHub →→ gwas_bridge.py →
Freelance · AI Training

AI Model Training & Evaluation

Expert-level RLHF, red-teaming, and evaluation work for frontier AI systems — biology, genomics, and data science domain.

Contributed to AI model alignment and capability evaluation across platforms including Mercor and similar. Tasks span prompt engineering, response ranking, domain-specific red-teaming (bioinformatics, clinical genomics, statistics), and structured feedback for RLHF pipelines. Domain expertise in biology and ML makes evaluations unusually high signal.

Expert · Biology & genomics domain evaluationRLHF · Alignment & capability feedbackFrontier · Models evaluated
RLHFRed-teamingPrompt EngineeringLLM EvaluationBiology DomainStatistics
Consulting · Confidential

Biotech & Genomics Consulting

Independent consulting for biotech startups and genomics companies — ML pipelines, cloud architecture, regulatory strategy, and data science.

Delivered production-grade ML and bioinformatics solutions to multiple clients under NDA. Work spans agentic AI pipeline design, cloud infrastructure (AWS), clinical data analysis, regulatory documentation, and data science strategy. Typical engagement: 3–6 months, outcome-focused delivery. Member of the Biotech Connection Singapore consulting community (public profile).

Multiple · Biotech/genomics clients (NDA)End-to-end · ML pipeline to production deliveryGlobal · Remote-first, Singapore-based
AWSPythonLangGraphClinical GenomicsRegulatoryData Science
↗ BCS public profile →
PhD Research · NTU

Age-Dependent Hepatocyte Epigenomics

PhD: integrative RNA-seq + ChIP-seq + Hi-C pipeline revealing age-driven chromatin reorganisation in mouse liver.

Built end-to-end NGS analysis pipelines for transcriptome, histone modifications, and 3D chromatin at NTU. Identified H3K27me3 as a key age-dependent regulator. Same technical foundation — reproducible pipelines, multi-omic integration, careful statistics — now applied to agentic ML.

RNA-seqChIP-seqHi-C (3C-seq)R · BioconductorPythonk-means / GSEA
→ Thesis (DOI) →

Career

Experience

Senior Scientist / Engineer, AI/ML

Illumina

Jul 2025 — Present
Singapore
  • ›Contributing to internal genomics foundation model development and fine-tuning — applying domain expertise to model training, evaluation, and biological validation (NDA).
  • ›Lead Design Verification Testing (DVT) and regulatory validation for commercial diagnostic products (TSO500, NIPT16, VeriSeq) — FDA compliance, clinical-deployment quality.
  • ›Optimised TruSight Oncology 500 assay workflows for comprehensive genomic profiling in oncology.
  • ›Contributed to NIPT16 non-invasive prenatal testing product development and validation.
  • ›Designed clinical concordance studies — sensitivity, specificity, accuracy, LOD, reproducibility — for regulatory submissions.

Senior Scientist — Bioinformatics & Cloud

Mirxes

Jan 2022 — Jul 2025
Singapore
  • ›Built production-grade cloud data-analysis pipelines: 40%↓ compute cost, 50%↓ storage, 30%↓ turnaround time.
  • ›Analysed 10,000+ WGS and 3,000+ metagenomics samples with standardised workflows and rigorous QC.
  • ›Architected AWS infrastructure integrating 15 services to manage and process 1 PB of genomic data.
  • ›Led team of 5 scientists delivering clinical diagnostic assay workflows — 33%↓ analysis time.
  • ›Technical lead for Singapore National Precision Medicine project; identified SGD 0.5M in business opportunities.

Consulting Advisor & Community Lead

Biotech Connection Singapore ↗
2023 — 2024
Singapore (Volunteer)
  • ›Part of BCS’s consulting community — a Singapore non-profit network bridging academia, biotech industry, and startups.
  • ›Contributed expertise in NGS data analysis, AWS cloud infrastructure, and clinical diagnostics to the BCS consulting panel.
  • ›Supported knowledge transfer between academic research and commercial biotech organisations.
NGSAWSClinical DiagnosticsCommunityNon-profit

Scientist, Assay Development

Vela Diagnostics

Jul 2021 — Dec 2021
Singapore
  • ›Implemented automated verification procedures and regulatory documentation (NCR, CAPA, DR).
  • ›Shipped testing framework that cut non-conformance reports 30% and lifted product quality 15%.

PhD Research Fellow

Nanyang Technological University

2016 — 2021
Singapore
  • ›Multi-omic study of age-dependent transcriptional and epigenetic changes in mouse hepatocytes.
  • ›Built reproducible NGS pipelines for RNA-seq, ChIP-seq, and Hi-C (3C-seq).
  • ›Head of NTU 3MT team (23 members, Nanyang Awards); TEDxNTU operations lead (78-person team, 1,500+ audience).

Technical Supervisor

Zydus Cadila Healthcare

2011 — 2013
Ahmedabad, India
  • ›Built process-validation and QC pipelines for 13 pharmaceutical products under cGMP — 30%↑ efficiency.

Research & Writing

Publications

LinkedIn Newsletter · Weekly · 2026

Slightly Intelligent — Smart AI insights, zero artificial fluff

Weekly newsletter cutting through AI hype with rigorous takes on AI in drug discovery, biology foundation models, and LLM evaluation. 8 editions published. Featured articles: "Why are AI-designed drugs clearing Phase 1 at 90% and still failing at Phase 2?" · "Why did a 2018 probabilistic model just outperform every single-cell foundation model?" · "Can Claude Science produce a validated analysis, or is it just faster at being wrong?"

Subscribe on LinkedInAI drugs & Phase 2 failure →2018 model beats foundation models →
Technical Blog · AWS Builder Center · 2025

Using Serverless for Cross-Organization Information Exchange in Genomic Analysis

Published on the AWS Builder Center. Serverless architecture patterns for sharing genomic data across organizational boundaries at scale.

Read on AWSMirror (community.aws)
Technical Blog · Personal · 2026

When an AI Agent Boards a Cruise Ship: Hantavirus, LangGraph, and Outbreak Triage

Deep dive into outbreak-agent architecture — built around the April 2026 MV Hondius/Andes virus event, the first confirmed human-to-human hantavirus transmission on a cruise ship.

Read post
Technical Writeup · Open Source

Why Agentic AI for Genomics? Designing Reasoning-Traceable Variant Interpretation

Design philosophy & architecture shipped with agentic-genomics. The case for agents over pipelines for judgment-heavy biomedical reasoning.

Read on GitHub
Doctoral Thesis · NTU · 2021

Age-Dependent Transcriptional and Epigenetic Alterations in Mouse Hepatocytes

Sharma, A. (2021). Nanyang Technological University, Singapore.

doi:10.32657/10356/155390hdl.handle.net
Peer-Reviewed · Frontiers in Microbiology · 2018

Antiproliferative and Antioxidative Bioactive Compounds in Extracts of Marine-Derived Endophytic Fungus

Kumari M, Taritla S, Sharma A, Jayabaskaran C. Frontiers in Microbiology, 9:1777. 108+ citations.

doi:10.3389/fmicb.2018.01777PubMedFull text
Conference Poster · Cell Symposia, Chicago · 2019

Significance of Hepatocyte Polyploidization in Liver Physiology and Pathology

Sharma A, Ong A, Wuestefeld T, Sanyal A. Transcriptional Regulation in Evolution, Development and Disease.

Toolkit

Skills

10+ years of stacking domain knowledge — agentic AI, ML, clinical genomics, cloud, and the science underneath.

CoreFrequentFamiliar

Agentic AI & LLMs

Primary

LangGraph / LangChainClaude / Anthropic APIMulti-agent orchestrationRAG & vector storesTool-calling & function agentsRLHF & LLM evaluationOpenAI API / GPT-4Local LLMs (Ollama)

Machine Learning & MLOps

Primary

Python · scikit-learnPandas / NumPyStatistical modellingPyTorchXGBoost / SHAPFeature engineeringMLflow / experiment tracking

Data Science

Primary

Survival analysis (Cox PH)Pan-cancer expression (TCGA)GO / KEGG enrichmentR · Bioconductor · DESeq2Matplotlib / seaborn / ggplot2Jupyter · reproducible analysis

Cloud & Infrastructure

Primary

AWS (Batch, Lambda, S3, Step Fns)Nextflow DSL2DockerCI/CD (GitHub Actions)IaC (CloudFormation / CDK)Kubernetes

Bioinformatics Domain

Expert

NGS — WGS / WES / RNA-seq / ChIP-seqDRAGEN · GATK · samtools · bcftoolsVariant calling & interpretationSingle-cell · spatial · WGMSSnakemake

Regulatory & Clinical

Expert

Design Verification Testing (DVT)Clinical validation (LOD, sensitivity)FDA compliance · SOPscGMP documentationNCR / CAPA / DRHIPAA · security compliance

Get in touch

Contact

Selectively open to senior roles in Agentic AI, ML Engineering, and Clinical Genomics globally. Also available for high-impact consulting engagements. Reach out any time.

✉

Email

ankurs103@gmail.com

💼

LinkedIn

linkedin.com/in/ankurit

⌥

GitHub

github.com/ankurgenomics

📱

Phone / WhatsApp

+65 8402 6093

↓

Download CV

PDF · 2026

📅

Book a 30-min call

cal.com/ankur-b-sharma

Open to the right opportunity

Singapore PR · Open to relocation · Remote-friendly · Senior / Staff / Principal level

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