AI Engineer

2 semanas atrás

Brasil Prosigliere Tempo inteiro

We're looking for a Senior ML/AI Engineer to own and evolve our LLM-powered user experience. You'll work directly with our technical co-founder to build, optimize, and monitor agent systems that parse workout descriptions, provide scaling recommendations, and enable conversational data retrieval - all with production-grade accuracy and speed.

This is a hands-on role focused on the ML/AI engineering side: prompt engineering, model optimization, agent orchestration, and continuous improvement based on real-world usage patterns.

What You'll Do

Core Responsibilities

- Own the workout parsing system: improve accuracy of our fine-tuned model (currently Qwen-based) that converts natural language workout descriptions into structured schemas
- Design and implement agent workflows for workout scaling recommendations and performance tracking
- Build observability workflows using Langfuse to identify and systematically address model performance issues
- Optimize agent response latency while maintaining accuracy across our tool-based reasoning system
- Collaborate on agent architecture decisions, including potential migration to frameworks like DSPy
- Ship production features: workout entry system, scaling recommendations, and score reporting

What We're Looking For

Required

- 5+ years of ML/AI engineering experience with at least 2 years working with LLMs in production
- Strong prompt engineering and model optimization skills
- Experience building and deploying agent systems with tools/functions
- Proven ability to use observability platforms to diagnose and improve model performance
- Experience with model fine-tuning (any framework/approach)
- Strong Python programming skills
- Active CrossFit participant - candidates should understand standard movements and workout structures

Strongly Preferred:

- Experience with agent orchestration frameworks (DSPy, LlamaIndex, or similar)
- Background in production ML operations and monitoring
- Experience with Modal.com or similar serverless ML platforms
- Track record of iteratively improving LLM systems based on user feedback and metrics
- Experience fine tuning similar open-source LLMs

Success in First 6 Months

- Ship workout entry system with improved parsing accuracy
- Launch basic workout scaling recommendations
- Implement user score reporting and retrieval
- Establish robust monitoring workflows to catch and address model failures and poor user experiences
- Contribute to agent architecture decisions as we scale

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