Senior Development Lead AI RAG Platform - Software Mind

Project - the aim you'll have

Software Mind is building a private, tenant-isolated AI assistant for the real estate title and settlement industry. The platform is a retrieval-first (RAG) system that ingests historical email, documents, and structured metadata into a per-tenant vector index, and serves grounded, cited, expert-weighted answers through a chat-style Q&A interface with single sign-on and full audit logging. The platform is AWS-native with a Python/FastAPI backend, Vue.js frontend, OpenSearch/Pinecone vector store, and OpenAI/Anthropic/Bedrock as LLM provider. You will join a senior, cross-functional LATAM-based team where hands-on AI delivery experience, not just familiarity, is the baseline expectation. You are the technical delivery lead, the bridge between architectural intent and day-to-day engineering execution. You own code quality, technical decisions within the team, and the delivery of the core AI Extraction Gateway (Simple and Complex RAG). You are hands-on: coding, reviewing, and unblocking across the Python backend and retrieval layers.

Expectations - the experience you need

• +90% English written and oral (at least B2 level) with excellent communication skills
• 6+ years in software development; minimum 2 years in a tech lead or senior engineering lead capacity
• Strong Python development skills; FastAPI or equivalent async Python framework required
• Hands-on AWS experience: ECS and/or Lambda, API Gateway, DynamoDB, S3, CloudWatch, X-Ray
• Experience with vector databases OpenSearch, Pinecone, Weaviate, or equivalent
• Solid understanding of API design, service decomposition, and clean backend architecture
• Delivered at least one production RAG, semantic search, or LLM-integrated application end-to-end, not a prototype or internal tool
• Practical experience integrating with LLM provider APIs (OpenAI, Anthropic, or Amazon Bedrock) in a production or enterprise configuration
• Working knowledge of chunking strategies, embedding models, retrieval ranking, and prompt engineering in a production context
• Experience with confidence scoring, retrieval evaluation, or hallucination mitigation approaches in a deployed system
• Experience with LangChain, LlamaIndex, or similar LLM orchestration frameworks (Nice to have)
• Familiarity with OCR pipelines and document extraction tooling (AWS Textract, Tesseract, or equivalent - Nice to have)
• Exposure to multi-tenant data isolation patterns and tenant-scoped encryption key management (Nice to have)

Position - how you'll contribute

• Lead hands-on development of the AI Extraction Gateway, progressing from Simple RAG to Complex RAG
• Implement and tune the expert-weighted (SME) retrieval layer and structured result validation
• Own confidence score calibration; collaborate with the BA on accuracy rubrics a