Principal Software Engineer · Austin, TX

Angelo Manos

I build the platforms teams ship on — backend systems, cloud infrastructure, and the ML pipelines that keep models honest between training and production.

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About

I'm a full-stack engineer with fifteen-plus years of shipping production software, most of it on the backend and the infrastructure underneath it. The part of the job I've gotten best at is breaking a fuzzy goal into a plan a team can execute — service boundaries, data model, delivery pipeline — and then following through until it's running, observed, and boring.

Lately that work has been service-oriented, event-driven platforms on GCP and AWS: Terraform-managed infrastructure, GitOps pipelines that spin ephemeral environments up and down, Kafka and Pub/Sub for the messaging layer, OpenTelemetry across everything. And increasingly it has been machine-learning infrastructure — feature stores, streaming features, model packaging and serving — because that is where I keep finding the biggest gap between what gets built and what stays working.

Off the clock: music, art, skateboarding, and a long-running habit of self-hosting things on a single NixOS node that I probably should have put on a managed service.

Skills

Languages

  • TypeScript / Node.js
  • Python
  • Java / Kotlin / Scala
  • Go
  • Rust (supporting binaries)
  • C / C++
  • Ruby
  • PHP
  • Bash
  • SQL
  • PromQL

Cloud & platform

  • GCP
  • AWS
  • Kubernetes (EKS, k3s)
  • Helm / Helmfile
  • Flux GitOps
  • Terraform
  • NixOS
  • Docker
  • Cloud Run
  • Cloudflare Workers & Zero Trust
  • Linux
  • nginx / Envoy Gateway

Data & streaming

  • PostgreSQL
  • BigQuery
  • ClickHouse
  • Apache Kafka / Redpanda
  • Apache Flink
  • Redis / Valkey
  • Elasticsearch
  • Cassandra
  • DynamoDB
  • DuckDB
  • Parquet / S3
  • MinIO

ML platform

  • Feast feature store
  • Point-in-time training sets
  • Streaming features (Flink → online store)
  • XGBoost → ONNX
  • ONNX Runtime serving
  • Training/serving skew detection
  • Kubeflow / Ray
  • Metaflow
  • Model release workflows

Services & APIs

  • OpenAPI spec-first
  • gRPC / protobuf
  • Fastify
  • Spring Boot
  • Express
  • Ruby on Rails
  • Event-driven architecture
  • Casbin RBAC
  • JWT / OAuth

Frontend & mobile

  • React 19
  • Vite
  • Zustand / TanStack Query
  • Tailwind
  • Angular
  • Flutter / Riverpod
  • Google Maps Platform

Delivery & observability

  • GitHub Actions
  • Jenkins (JCasC, shared libraries)
  • CircleCI
  • OpenTelemetry
  • Prometheus / Grafana
  • Jaeger / Loki
  • Datadog / New Relic
  • Playwright e2e
  • TDD

ML platform & MLOps

Models are easy. Keeping them honest is the work.

Most ML incidents aren't model bugs — they're a feature computed one way in the notebook and another way in the service, or a training set that quietly saw the future. I build the layer that prevents that: one feature schema, enforced on every write and read path, from the batch job to the request handler.

Feature stores as the contract

Feast-backed feature platforms where the registry is the single source of truth: batch and stream producers validate against it before they write, training and serving read the same versioned feature service.

Point-in-time correct training

Training sets assembled with as-of joins so every row only sees what was knowable at the event's timestamp — no leakage, no AUC that evaporates in production.

Streaming features

Flink jobs over Kafka with event-time windows and watermarks, pushing near-real-time aggregates into the online store through the same schema the offline path uses.

Packaging & serving

XGBoost and friends exported to ONNX and served from JVM services with ONNX Runtime — one artifact, no Python at the edge, feature vectors assembled from the online store at request time.

Model SDLC

At Harman I partnered with ML engineers to design the end-to-end training and inference SDLC — CI/CD, config management, release workflows, code standards — cutting delivery time and deployment errors by roughly half.

Skew detection & observability

Served-feature logging joined back to the offline reconstruction, OpenTelemetry traces through the inference path, and dashboards that catch training/serving drift before the business metric does.

Read the feature-store walkthrough →

Experience

  1. Sep 2024 — present

    Sr. Principal Software Engineer · Harman International

    • Architected a service-oriented, event-driven platform on GCP: Terraform-managed, multiple permanent and ephemeral environments, services and jobs on Cloud Run, Pub/Sub as the message bus.
    • Built and ran production TypeScript and Python REST APIs (OpenAPI-driven) with supporting Rust binaries, PostgreSQL, BigQuery for OLAP and Redis caching, developed under TDD.
    • Partnered with ML engineers to design and operationalize the end-to-end model training and inference SDLC, cutting delivery time and deployment errors by ~50%.
    • Designed cross-project, multi-environment GitOps CI/CD so developers spin environments up and down safely under least-privilege access; misconfiguration incidents dropped and delivery frequency rose sharply.
    • Standardized OpenTelemetry instrumentation across all services and provisioned end-to-end observability (metrics, traces, dashboards, alerting) via Terraform.
    • Shipped threaded comments, search, a high-throughput event-driven messaging system, digital-asset license validation, and edge services on Cloudflare Workers with Zero Trust database access.
  2. Jun 2023 — Sep 2024

    Principal Software Engineer · App Lattice

    • Designed a vendor-agnostic, declarative service platform on AWS EKS with Kubernetes, Helm and Helmfile.
    • Built and maintained gRPC microservices in TypeScript/Node.js on an event-driven Kafka backbone; ran PostgreSQL, Cassandra and Redis clusters behind them.
    • Owned Jenkins CI/CD (JCasC + shared libraries) and a self-hosted observability stack — OpenTelemetry, Prometheus, Grafana, Jaeger, Loki.
  3. Sep 2021 — Jun 2023

    Staff Software Engineer · HealthSherpa

    • Performance work on a Ruby on Rails + PostgreSQL backend that directly helped over a million more people enroll in health coverage.
    • Owned major backend projects end to end: RFCs, technical design, scoping and estimation; designed horizontally scaling AWS infrastructure for millions of requests per second.
  4. Aug 2019 — Sep 2021

    Sr. Principal Software Engineer · Standard Conversions

    • Set the engineering practices for a startup — PR and review process, quality guidelines, deployment and disaster-recovery procedures.
    • Built TypeScript ETL pipelines ingesting large time-series datasets from third-party APIs into Elasticsearch for BI; led the migration of a PHP monolith to a Spring Boot distributed system.

Projects

feast-demo — feature store, end to end

A batteries-included ML platform in miniature: synthetic events → DuckDB batch jobs → Feast offline/online stores, a Flink job pushing streaming features, XGBoost trained on point-in-time joins, exported to ONNX and served from Spring Boot. Runs on a local kind cluster with Nix.

  • Feast
  • Flink
  • Kafka
  • DuckDB
  • XGBoost
  • ONNX
  • Spring Boot
  • Terraform
  • kind

MiSpots

A location-based social platform I run end to end: OpenAPI-first Fastify API, React map app, Flutter port, admin dashboard, streaming video pipeline, and a notifier service on Kafka — deployed by Flux onto a NixOS k3s node provisioned with Terraform, with per-branch disposable environments and an unattended Jira-to-PR coding agent.

  • TypeScript
  • Fastify
  • Prisma
  • React
  • Flutter
  • k3s
  • Flux
  • Envoy Gateway
  • NixOS
  • Terraform
  • ClickHouse
  • Redpanda

msx — environments as a CLI

A developer-experience CLI for MiSpots: one command stands up an isolated droplet with its own k3s, Flux branch, DNS and TLS, seeds it, and tears it down; a drift guard in CI fails the build when infra changes outpace the tool.

  • TypeScript
  • Terraform
  • DigitalOcean
  • Flux
  • GitHub Actions

Self-hosted platform

Everything above runs on infrastructure I own: NixOS nodes, a WireGuard admin plane, SOPS-encrypted secrets reconciled by Flux, nightly backups to object storage, and image automation that rolls deployments from CI-tagged images.

  • NixOS
  • WireGuard
  • SOPS / age
  • Flux image automation
  • MinIO
  • PostgreSQL

Writing

Contact

The fastest way to reach me is email: angelo@angelomanos.com. I'm also on GitHub and LinkedIn.