
Oliver Muellerklein¶
I build agentic systems end-to-end: LLM-driven extraction pipelines, semantic search, custom MCP tooling, and the full-stack applications that wrap them. Background in environmental science, geospatial data, and HPC.
Selected work¶
A full-stack agentic CRM I built for a beekeeping wholesale domain. Custom chatbots, an automated ordering pipeline that converts inbound email into typed orders, and a semantic-search RAG layer over operational data. Live app, source code, and architecture diagrams.
One decision chain from my TRACE records. Boxes are records and arrows are the links stored in them. Select a box to read the record.
Skills¶
LLM tool use, prompt engineering, eval design
Custom MCP server design (Python)
LangGraph + ReAct agents
RAG, semantic search, embeddings (pgvector)
Decision provenance + audit trails (TRACE)
Python: FastAPI, Pydantic, async pipelines
JavaScript / TypeScript, Clojure / ClojureScript
Postgres + pgvector, SQL, Alembic migrations
FSM design, idempotent ETL, schema-first ingest
Pyright, type-driven Python; Pytest with real-data fixtures
AWS (EC2, SageMaker), Azure, GCP
HPC: Slurm, batch jobs, distributed compute
Containers: Docker, Kubernetes
Apptainer (renamed from Singularity, 2021)
Reproducible env management across cloud + on-prem
PostGIS for spatial SQL
Google Earth Engine: low-level API + scripts
GDAL / OGR, raster + vector workflows
Interactive web maps + geospatial models in Python, JavaScript, Clojure / ClojureScript
Satellite imagery (Sentinel, Landsat), time-series rasters, CRS handling
R + RStudio: extensively, ecological + environmental modelling
Python: pandas, scikit-learn
Time-series and spatial statistics
Reproducible analysis pipelines
E2E testing against real data over mocks
Fail-loud over silent error swallowing
Schema-first design, Pydantic across boundaries
Open-source MCP servers and tooling
TRACE-style decision provenance for AI work