Principal AI Engineer (Platform team)

Improvado•, GA

5d•Remote

About The Position

We're a fast-growing Series A startup building the unified marketing intelligence platform for enterprises. Our Platform Team owns the core data pipeline infrastructure powering 500+ integrations and is rebuilding how that work gets done, with AI at the center. We are looking for a Principal Engineer to set the technical direction for AI-assisted engineering across the Platform Team, defining how AI tools are used responsibly at scale. This role involves shaping the design, build, and evolution of our core Python-based extraction and load infrastructure, and leading the shift in engineering practices by integrating agentic coding, AI-assisted development, and LLM tooling into our delivery process without sacrificing reliability or maintainability. This is a hands-on role requiring coding, architecture review, and mentorship.

Requirements

7+ years of backend engineering (hands-on programming), with at least 2 years in a principal/architect-level role owning production systems
Designed and scaled complex systems, not just maintained them
Hands-on experience with AI-assisted development (Claude Code, agentic coding) — you ship with it, know its limits and where they break, and have built agentic frameworks for code maintenance and rollout
Strong distributed systems background: Kubernetes, workflow orchestration (e.g. Temporal), message queues (e.g. RabbitMQ)
Experience building secure systems that are hard to break, hard to breach, and resilient
Experience with reliability practices at scale: observability, alerting, incident response
Solid SQL and query optimization across PostgreSQL and ClickHouse
Comfortable with ambiguity and high-velocity, fast-shifting priorities
Ability to work within EST timezone
Fluency in English

Nice To Haves

Track record of defining engineering standards and driving adoption across a team
Experience migrating legacy pipeline systems
Background in data integration or ETL infrastructure

Responsibilities

Own platform architecture: design and evolve data extraction, load, and pipeline systems serving 500+ integrations at scale
Identify architectural bottlenecks and drive performance/reliability improvements at scale (Kubernetes, distributed systems, ClickHouse/PostgreSQL)
Lead modernization of legacy systems (LES, DSAS, ULS) into scalable, AI-native infrastructure
Design and implement agentic development frameworks for codebase maintenance and continuous improvement — owning the full loop from AI-assisted development to automated acceptance and rollout, with proper validation, testing, and architectural guardrails
Make and document key technical decisions; set the bar for code quality, system reliability, and performance
Mentor and level up engineers on AI-assisted development practices — help them ship faster without introducing hidden risk
Collaborate cross-functionally with product and AI teams on agentic framework rollout and new integration capabilities