5 months ago
Mexico City, MexicoSenior
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 AI evaluation practices.
- Design vector search, document ingestion, chunking, embedding, metadata filtering, and re-ranking pipelines.
- Take trained ML models from experimentation into production serving infrastructure and maintain inference endpoints, monitoring, and retraining workflows.
- Integrate AI services with backend microservices using API contracts, circuit breakers, graceful degradation, logging, tracing, dashboards, and alerting.
- Build human-in-the-loop review workflows for high-value financial decisions.
- 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.
- Production experience building and deploying LLM-powered applications using APIs such as OpenAI, Anthropic, or Cohere.
- Experience designing and operating RAG pipelines with chunking strategies, embedding models, and vector database integration.
- Strong Python proficiency for AI/ML workloads 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 engineering experience with APIs, relational databases, asynchronous patterns, and cloud infrastructure.
- Experience with observability tooling, structured logging, distributed tracing, and AI system health dashboards.
- Preferred: experience in fintech, financial services, regulated industries, prompt evaluation, AI output A/B testing, and production model performance tracking.
- Preferred: experience with MLflow, Weights & Biases, Vertex AI, SageMaker, Kafka, or Kinesis.
- Preferred: open-source AI tooling contributions, published technical writing, AI/ML conference talks, and startup or scale-up experience.
Benefits
- Full-time remote position.
- Opportunity to build foundational AI systems for a global financial technology platform.
- Work with backend engineers, data scientists, and product teams on production financial AI applications.
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