Juan C.

I build AI agent systems that run real business operations — not demos. At a US
construction-technology marketplace I architected the agent platform behind 25+
production automations.

Most engineers can call an LLM API. The hard part is everything around it:
concurrency, idempotency, state that survives a restart, and data underneath that
you can actually trust. I've spent 6 years on both halves of that problem.

── AI & AUTOMATION ──

• Agent platforms — FastAPI services on Kubernetes orchestrating agent harnesses
(OpenCode, Claude Agents SDK, Pi) with concurrency locks and autoscaling, plus
higher-level abstractions that let non-technical staff define their own workflows.
25+ automations now running across the business.

• LLM automation of real workflows — heuristics + LLM triage over SQS/SNS and
multi-step AWS Lambda, routing each issue to the right engineer in Jira or Slack.
~400 engineering hours saved per month.

• RAG systems — vector search over authoritative documents (Weaviate, OpenAI
embeddings) driving real-time suggestions inside an existing workflow.

── DATA PLATFORMS ──

• CDC & replication — I architected a company-wide PostgreSQL→BigQuery CDC platform
using audit triggers, watermark-based extraction and idempotent partition-bounded
MERGE loads. 4+ years in production at a 15-minute freshness SLA, near-zero
maintenance.

• Warehouse cost control — cut BigQuery spend by $35k/year through partitioning,
clustering, pre-aggregated tables and per-query cost attribution, without
restricting analyst access. Held for 3+ years.

• Pipelines & modeling — Airflow on AWS (1,500-metric pipeline), dbt, star schemas
with SCD2, Kafka/Redpanda with Avro and schema-registry contracts, Iceberg, Spark.

── BACKEND ──

Python (Django, FastAPI) and Go on AWS and GCP. Event-driven services with the data
contracts, idempotency and graceful shutdown that keep them correct under load.

── HEALTHCARE ──

I'm also a qualified physician (MD, 2019) before I moved into engineering, which
makes me unusually effective on clinical software: I've built a multi-tenant practice-management system for a multi-clinic gynecology practice (Django, React, PostgreSQL), and won first place at the Saptiva AI Hackathon for a RAG system that suggests evaluations in real time as a doctor completes a medical record. If your project touches clinical workflows, you won't have to explain the domain to me.