- Senior software engineer with 20+ years of experience building large-scale backends, data pipelines, and distributed systems.
- I specialize in Python, from high-performance microservices and REST APIs, to data engineering workflows, to AI/ML integrations, to everything in between.
- I care deeply about code quality and have a track record of establishing best practices and mentoring teams.
- Python Ecosystem: Asyncio, Type Annotations, Pydantic, Pytest
- Web & API Frameworks: FastAPI, Litestar, Django, Flask, SQLAlchemy
- Databases & Storage: MySQL, PostgreSQL, SQLite, MongoDB, Redis, Elasticsearch
- Data Engineering: Pandas, Dask, Spark, Airflow, Prefect
- Machine Learning & Scientific Computing: NumPy, PyTorch, TensorFlow, Scikit-learn
- DevOps & Infrastructure: Docker, Docker Compose, GitHub Actions, Ansible, Bash
Current Status
Between career break, funemployment and contemplating early(-ish) retirement. May consider getting back in action for interesting, meaningful opportunities that don't revolve around coding agents. Potential exception: initiatives that treat agentic coding as a liability to be contained, trying to address issues including but not limited to:
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Code Quality & Technical Debt: working on tools and practices that keep AI-generated code honest, based on the radical premise that working software should also be maintainable by humans.
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Cognitive & Comprehension Debt: bridging the gap between "the code seems to work" and "nobody beyond perhaps the one who prompted it knows why and how", without reverse-engineering intent from reams of auto-generated slop that was outdated by the next commit.
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Review Infrastructure: designing workflows that preserve genuine human oversight, as opposed to an engineer's reflexive "LGTM" after scrolling over a 4000-line PR for 10 seconds.
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Test Integrity: ensuring that "tests pass" remains a strong signal of confidence rather than security theater played by agents adding dummy mocks or rewriting failing tests to match broken behavior.
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Engineering Culture & Developer Experience: tracking the cost of top-down AI adoption mandates for the people doing the work, because that's nobody's official problem yet.