5 months ago
Buenos Aires, ArgentinaSenior
Responsibilities
- Design, build, and maintain production LLM integration pipelines using RAG, prompt engineering, output parsing, and chain orchestration.
- Develop AI features for spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
- Implement structured output validation, fallback handling, confidence scoring, prompt versioning, and evaluation practices.
- Design vector search, document ingestion, chunking, embedding, metadata filtering, and re-ranking pipelines.
- Collaborate with data scientists to deploy ML models, build inference endpoints, monitor performance and data drift, and support retraining workflows.
- Integrate AI services with backend microservices and build reliable backend code in Python or Go/Node.js.
- Implement structured logging, distributed tracing, dashboards, alerting, and human-in-the-loop workflows for AI system oversight.
- Partner with Product, Backend Engineering, and Data Science on the AI roadmap and establish reusable AI engineering practices.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 5+ years of professional software engineering experience, including at least 3 years focused on AI/ML systems in production.
- Hands-on experience building and deploying production LLM applications using APIs such as OpenAI, Anthropic, or Cohere.
- Experience designing and operating RAG pipelines with chunking strategies, embedding models, and vector databases such as Pinecone, Weaviate, or pgvector.
- Strong Python proficiency and familiarity with an AI orchestration framework such as LangChain or LlamaIndex.
- Experience with ML model serving infrastructure, inference endpoints, validation, latency budgeting, and monitoring.
- Backend fundamentals including APIs, relational databases, asynchronous patterns, and cloud infrastructure on AWS, GCP, or Azure.
- Experience with observability tooling, structured logging, distributed tracing, and AI system health dashboards.
- Preferred qualifications include fintech or regulated-industry experience, prompt evaluation and A/B testing experience, ML lifecycle tooling knowledge, real-time streaming experience, open-source or technical writing contributions, conference talks, and startup or scale-up experience.
Benefits
- Full-time remote position.
- Opportunity to build foundational AI systems for a global financial operating system serving businesses across more than 20 countries.
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