Principal Engineer & Architect - AI Agent Plaform

DevSavant Inc.; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

DevSavant Inc. seeks a senior software architect to own the platform's architecture, data models, and AI agent systems. You will guide distributed engineers, define standards, and drive secure, scalable infrastructure for production workloads across multiple modules.

You will collaborate with clients and partners, shaping the technical direction and ensuring high availability, security, and performance of the managed AI agents product.

Qualifications

• 8+ years of production software experience, including ownership of a major system's architecture.
• Deep experience with TypeScript/Node.js and modern frontend ecosystems.
• Proven track record shipping production LLM agent systems with guardrails, monitoring, and incident handling.

Responsibilities

• Design whole-platform architecture and data flows for client integrations and dashboards.
• Lead production AI agent systems, including guardrails, monitoring, and tool usage policies.
• Own cloud & infrastructure direction, CI/CD, observability, cost management, and tenancy isolation.
• Define security standards for multi-tenant environments and audit trails.
• Set code standards, conduct reviews, and guide distributed engineering teams.
• Engage with clients and design partners as technical lead on complex discussions.

Skills

Architectural leadership

Communication

English proficient

Spanish proficient

Tools

TypeScript

Node.js

React

Remix

Tailwind

ShadCN

Drizzle

Zod

PostgreSQL

Redis

PgBoss

AWS or GCP

Job description

About DevSavant

DevSavant builds and operates managed AI agents / AI coworkers that run real business workflows in production for SaaS companies. We are turning that service into a scalable product: connecting client systems, normalizing their data, and running agent-driven workflows with human approval, dashboards, and full auditability.

Role Overview

You will be the senior-most engineer on the platform and its technical architect. You will design the integrations, data layers, APIs, infrastructure, security model, and the AI agents themselves. You present those designs to the Director of Engineering for sign-off, and then own building and shipping them alongside a distributed team of senior engineers.

How authority works here: you architect and propose, the Director of Engineering signs off on the direction, and you own execution, technical standards, and team direction from there. This is a hands-on architectural and technical leadership role. You will be writing code on the hard parts of the system, not managing HR functions or line-by-line supervision.

You will start by owning the architecture for the core platform (Managed AI Agents). As we expand into new business modules (Company Brain), you will take ownership of their technical architecture too, stepping in across projects to guide distributed engineering teams based on business priorities.

Responsibilities

• Whole-Platform Architecture: Design how client systems connect, how data is normalized and stored, how information is served to dashboards and AI agents, and how new client tools get integrated efficiently. You will be hands-on, building the primary framework skeletons yourself.

• Production AI Agent Systems: Handle agent design, tool usage, guardrails, evaluations, and monitoring for agents executing business workflows with human oversight.

• Cloud & Infrastructure Direction: Lead deployment architecture, CI/CD, observability, and cost management. The platform runs on containerized infrastructure today; deciding where the runtime should live long-term is one of your first major projects.

• Security & Multi-Tenant Architecture: Own tenant isolation, secrets management, least-privilege access, and audit trails for a platform handling financial data. You define the standards, and the team implements them.

• Technical Leadership: Set code standards, conduct reviews, and guide a distributed bench of senior engineers through clear written specifications.

• Client Engagement: Serve as the technical point of contact with clients and design partners when complex architectural discussions require it.

Requirements

• 8+ years of production software experience, including recent hands-on ownership of a major system's architecture.

• Modern Tech Stack Expertise: Deep experience in TypeScript and Node.js ecosystems, modern frontends (React, Remix / React Router v8, Tailwind, ShadCN), relational ORMs (Drizzle), validation tools (Zod), databases (PostgreSQL, Redis), and queueing systems (PgBoss).

• Production LLM Agent Systems: Proven track record shipping and operating real LLM agent systems, not just prototypes. You have built guardrails, evaluated agents prior to launch, monitored them in production, and handled agent-specific failure modes like loops, silent stalls, context drift, and tool misuse.

• RAG & Engineering Depth: Deep understanding of RAG pipelines, chunking strategies, and vector data workflows.

• Production Infrastructure Ownership: Experience managing uptime, running CI/CD, setting up observability, and handling production incidents end to end.

• Cloud Architecture (AWS or GCP): Proven experience making and defending decisions around platform hosting and runtimes.

• Production API Integrations: Experience with complex third-party API environments involving webhooks, incremental polling, idempotency, retries, data

• normalization, and data reconciliation.

• Data Modeling: Strong experience with relational and event-driven data designs where data accuracy is the priority.

• Multi-Tenant SaaS Security: Practical experience with tenant isolation, secrets hygiene, least-privilege access control, and audit logs.

• Written-First Leadership: Ability to write clear, complete technical specifications that allow remote engineering teams to execute high-quality work asynchronously.

• Communication: Excellent written and spoken English and Spanish.

Nice-to-haves

• Official AWS or GCP Cloud Architect certifications.

• Experience with agent frameworks beyond single-vendor stacks (such as OpenClaw, MCP, or A2A).

• Experience with Infrastructure as Code and observability tools like Prometheus or Grafana.

• Background in graph databases and knowledge graphs.

• Early-stage startup experience building new product lines from 0→1.