# Aurelio Leal

> Senior Software Engineer at Avoma

Location: Santa Clara, California, United States
Profile: https://flows.cv/aurelioleal

Senior Full-Stack Engineer with 10+ years building SaaS products across frontend, backend, and cloud. I ship customer-facing features, dashboards, and workflow automation, and I care a lot about reliability and clean UX.

Recent work includes real-time ingestion / transcription workflows, CRM/platform integrations, and internal developer tools that improved support speed and data quality. I’m strongest with TypeScript/Node.js, React/Next.js, SQL/Postgres, and AWS, and I’m comfortable owning projects end to end—from design to rollout to production support.

I’m currently targeting Senior Full-Stack roles (SaaS/product teams), especially work involving integrations, real-time features, and system reliability.

## Work Experience
### Senior Software Engineer @ Avoma
Jan 2023 – Present | Palo Alto, CA
- Designed and shipped a new real-time meeting bot architecture for AI recording/analysis, reducing bot join latency by 65% and improving reliability across edge cases in production.

- Built a real-time streaming ingestion pipeline (Zoom RTMP) to power transcription workflows, cutting end-to-end processing latency by 30% and improving throughput under load.

- Implemented resilient streaming and asynchronous processing patterns (backpressure, retries, graceful recovery) to keep real-time pipelines stable during network variance and provider hiccups.

- Shipped instructor/admin-like internal tooling (impersonation + diagnostics) that accelerated QA, debugging, and production support investigations.

- Delivered and maintained third-party platform integrations that synchronized structured meeting outputs, improving downstream usability and data consistency.

- Partnered with product to iterate quickly on customer-facing workflow UX, balancing rapid shipping with reliability and maintainability.

### Founding Software Engineer @ Plenful
Jan 2021 – Jan 2023 | San Francisco, CA
- Built and scaled an AI-assisted ingestion pipeline (classification, splitting, extraction) converting unstructured PDFs/faxes into structured, workflow-ready data.
- Integrated LLM/AI APIs into extraction and validation steps to increase automation coverage and reduce manual review, with clear fallbacks for uncertain cases.
- Migrated long-running ingestion and automation to Temporal, improving reliability with durable execution, retries/timeouts, and safer scaling under high volume.
- Owned features end-to-end across UI, backend services, and infrastructure—shipping fast while maintaining clean handoffs between review screens and automated workflows.
- Strengthened observability and operational resilience (failure handling, edge-case recovery, performance tuning) to keep production workflows diagnosable and stable.
- Worked directly with customer technical stakeholders to refine requirements, validate behavior, and deliver low-risk rollouts.


## Education
### Bachelor's Degree in Computer Science
The University of Texas at Austin


## Contact & Social
- LinkedIn: https://linkedin.com/in/aurelio-leal-93165b3b3

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Source: https://flows.cv/aurelioleal
JSON Resume: https://flows.cv/aurelioleal/resume.json
Last updated: 2026-04-01