Equips AI Engineer

Role overview

This senior contract role centers on building production agentic AI systems for an early-stage platform that equips field service technicians with on-site knowledge to resolve complex equipment issues. The product already serves live users and is rolling out to its first paying customers, so the work ships under real usage, real data, and real multi-tenant pressure from day one. The position is fully remote in Argentina, structured as full-time contract hours overlapping the US Central business day.

Responsibilities

- Design and ship AI-enabled applications end to end, spanning retrieval and agent orchestration through the supporting data pipelines - Own technical delivery across the full stack, from backend services in Python/FastAPI to modern web interfaces in TypeScript/React - Help define and operationalize evaluation practices, working with subject-matter experts to formalize what a correct answer looks like and catching quality regressions before users encounter them - Practice test-driven development and continuous refactoring so the codebase stays reliable, maintainable, and approachable for whoever picks it up next - Partner closely with designers, product managers, and client stakeholders to ship working software iteratively

Requirements

- Production experience with LLM systems, including RAG pipelines, vector databases, chunking strategy, and structured prompt or schema design - Hands-on experience building agentic workflows with tool calls and multi-agent orchestration using a real agent framework (LangChain, LangGraph, or comparable) - Demonstrated experience building repeatable, automated evaluations to catch quality regressions rather than ad-hoc output checks - Full-stack range across Python (FastAPI or equivalent) and TypeScript/React, with a track record of shipping production software on both sides - Strong written and spoken English, with full availability to overlap US Central business hours and work directly with US-based teammates and stakeholders - Track record delivering software in collaborative, agile, client-facing environments, paired with a genuine belief that code quality and pairing are how strong systems get built - Ability to explain technical risk in terms a non-technical stakeholder can act on, raising problems alongside possible solutions

Nice to have

- Experience with multi-tenant systems or a strong instinct for keeping customer data isolated and limiting blast radius - Data pipeline orchestration experience using tools like Prefect, Dagster, Airflow, or similar - Observability and tracing experience specifically for LLM and agent systems - Background in industrial, field service, or other domains where errors carry physical consequences - AWS infrastructure experience