Design and build AI-powered microservices (Python/FastAPI) for inference and decision automation.
Implement LLM workflows using LangChain, LangGraph, CrewAI, AutoGen, or MCP-based architectures.
Build RAG pipelines: ingestion, chunking, embeddings, retrieval, re-ranking.
Integrate AI features into core products (Payments, Collections, Billing, SIS).
Build agentic workflows for data reconciliation, anomaly detection, financial validation, compliance checks, and school-specific automations.
Design structured tool-calling, guardrails, and self-correction loops.
Implement event-driven AI pipelines connected to Kafka/SQS.
Implement and optimize vector stores (pgvector, FAISS, Weaviate, Milvus).
Create evaluation dashboards and automated evals to benchmark AI reliability.
Build observability around prompts, costs, latencies, and error handling.
Build scalable APIs integrating AI models with the existing microservice ecosystem.
Deploy AI services on AWS using ECS, Lambda, SQS, Aurora, Redis, S3.
Ensure security, compliance, PII protection, and safe AI usage.
Partner with Product and Operations to translate business needs into AI features.
Convert prototypes into robust, production-ready services.
Work with Data Engineering to power training, feature engineering, and retrieval.

4+ years of experience in software engineering, data engineering, or ML engineering.
1+ year hands-on building LLM-powered systems (agents, RAG, pipelines, evaluators).
Strong Python skills; experience with FastAPI preferred.
Experience with LangChain, LangGraph, CrewAI, AutoGen, or MCP.
Experience with vector DBs: FAISS, Milvus, pgvector, Pinecone.
Experience calling/integrating OpenAI, Anthropic, Mistral, Gemini, Groq, or similar APIs.
Familiarity with SQL and relational models (PostgreSQL/Aurora).
Strong understanding of REST/gRPC API design.
High ownership and bias for action.
Ability to take ambiguous problem statements and ship solutions fast.
Strong communication and collaboration skills.
Comfortable in a fast-paced, startup environment.
Full professional proficiency in Spanish and English (written & spoken).

AI Backend Engineer

Key skills