Computational Biology / Platform Engineering
Scientific systems that survive production.
Current Focus
Biology, ML, and infrastructure in the same room.
The work sits between scientific reasoning and production reliability: molecular modelling, NLP, data contracts, APIs, relational schemas, and operational pipelines.
Featured Projects
Selected work
DASH
Internal LeverageHR
Partner PlatformAgentic Tooling
Deploys itself. Asks before it touches the database.
A real run from one of the MCP servers I built so agents operate safely inside production systems — writing artifacts, opening tickets, and knowing exactly where to stop.
convert-mcp · reshape
MCP Servers
scoring-mcp
Score release pipeline — builds snapshots, generates release/deploy/rollback SQL, formats reference links, and sends Slack announcements.
sql-executor
Named, parameterized, pre-reviewed SQL queries for common lookups, plus a raw-SQL escape hatch for one-off schema exploration.
workflows-mcp
Serves named, multi-step procedural runbooks — no business logic, no side effects at all.
convert-mcp
Generic pandas-backed file format conversion — csv, excel, json, parquet, pickle, and more.
translate-ids-mcp
Cross-translates identifiers (kit-id, user-id, test-id, external-id) across internal databases, optionally joined to analysis-id.
expression-mcp
Pulls filtered expression data (by feature type and read-count threshold) from the bioinformatics database as a clean, ready-to-analyze CSV.
GitHub Activity
Past year
Language Mix
- Python42.5%
- Java38.8%
- HCL9.5%
- Shell2.8%
- R2.0%
- Go1.6%
- Other2.8%
What I Build
Production-grade scientific platforms.
Flask APIs, relational databases, scalable curation scripts, NLP models, multiomics analysis workflows, and molecular simulation tooling with clear handoff boundaries.
Looking For
Senior data science or platform roles.
Best fit: biotech, health-tech, AI drug discovery, precision medicine, or infrastructure teams building for scientific workloads.