Applied AI Engineer - Remote - $84K-$120K USD
LATAM Applied AI Engineer | Santiago del Estero Province, Argentina | Remote | Avg $84K–$120K USD
Role Overview
A fast-growing AI startup focused on the entertainment industry and creator economy is seeking a LATAM-based Applied AI Engineer to help scale and improve the reliability of production-grade multi-agent systems.
This is not a generic full-stack engineering role. The team is specifically looking for someone with real-world experience operating, debugging, and improving non-deterministic LLM systems under production traffic.
The ideal candidate understands how AI agents fail in real environments and has experience building evaluation pipelines, observability systems, scoring frameworks, and feedback loops that improve reliability at scale.
This role works closely with the technical co-founder on architecture decisions involving agent reliability, human-in-the-loop escalation systems, multi-channel routing, and production evaluation workflows.
About the Company
The company builds AI agents for the global entertainment industry and creator economy. Its platform is already being used in private beta by professionals associated with Netflix, WME, UTA, Live Nation, and other major entertainment organizations.
The founding and technical teams include leaders from companies such as NVIDIA, Intuit, HubSpot, Warner Music Group, Robinhood, and Hebbia AI. The company has raised significant early-stage funding and is rapidly scaling its AI infrastructure capabilities.
Key Responsibilities
AI Agent Reliability & Evaluation Systems
Instrument and monitor AI agent systems using observability tooling
Build and maintain evaluation datasets from real production traffic
Develop scoring pipelines focused on quality, robustness, and reliability
Improve human-in-the-loop escalation systems and failure handling
Analyze LLM behavior under real-world production conditions
Create feedback loops connecting evaluations, prompts, and architecture changes
Backend & Infrastructure Engineering
Build and maintain backend systems using Python technologies
Operate end-to-end across development, testing, deployment, and production sustainment
Support multi-agent orchestration systems and workflow infrastructure
Improve reliability across scheduling, messaging, and communication agents
Participate in architecture discussions involving AI reliability and scalability
Observability & Optimization
Work with logging, metrics, and monitoring systems
Evaluate and improve AI-assisted workflows and production tooling
Support experimentation around DSPy, DPO, and future optimization systems
Help scale evaluation infrastructure as the platform grows
Requirements
Real production experience working with non-deterministic LLM systems
Experience building or operating evaluation pipelines for AI systems
Strong Python backend development experience
Experience with FastAPI, Django, Celery, or similar backend technologies
Familiarity with LangGraph or similar agent orchestration frameworks
Experience with observability platforms such as Langfuse, Grafana, Loki, or equivalent
Ability to independently own projects from development through deployment and production support
Strong written communication and fluent English skills
Ability to work effectively in async remote environments
Preferred Qualifications
DSPy, DPO, or LLM optimization experience
Multi-agent systems and tool-use orchestration experience
Exposure to production multi-model stacks including GPT-4o, Claude, or Whisper
Previous startup or high-growth technology company experience
Background at strong LATAM technology organizations or YC-backed startups
Strong product-oriented engineering mindset
Compensation & Benefits
Avg Salary: $84,000–$120,000 USD
Fully Remote LATAM Opportunity
High ownership and technical autonomy
Direct collaboration with experienced founders and AI leaders
Opportunity to help shape production AI reliability systems at an early-stage company
Ideal Candidate
Deeply technical AI engineer focused on reliability and production systems
Comfortable working independently in fast-moving startup environments
Strong systems thinker with practical engineering judgment
Obsessed with improving LLM reliability and agent performance
Product-minded engineer who understands operational tradeoffs
Excited about the future of AI agents within the creator economy and entertainment industry