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Machine Learning Engineer
RealQuantarujá, estado de são paulo, brHá 8 dias
Descrição da vagapgvector Databricks (labeling + ETL) Azure Postgres Flexible Blob Storage Azure Durable Functions Event Grid (ingest triggers) Service Bus (optional) API Management (APIM) Key Vault (Managed Identity) GitHub Actions CI / CD Train and fine-tune LLMs + embeddings for OM, Rent Roll, and P&L parsing. Detect and learn document layout patterns to reduce human-in-loop (HITL) review. Build labeled datasets and evaluation benchmarks in Databricks + Azure ML. Integrate 3rd-party market data and create retrieval pipelines via Azure AI Search / pgvector. Develop the AI Underwriting Agent that generates deal summaries, LOIs, pitch decks, and assumptions. Deploy models via Azure ML / Durable Functions with scalable APIs. Optimize inference pipelines for sub-10s end-to-end latency , including preprocessing and write-back to Postgres. Track accuracy, recall, latency & cost with App Insights dashboards; iterate rapidly using Cursor / Claude Code + HITL QA. 3+ production-ready parsers (OM, Rent Roll, or P&L) deployed in Azure 100+ labeled examples + versioned evaluation datasets ≥95% extraction accuracy vs manual baseline AI-generated deal summary & LOI draft live in Excel / web Confidence-scored mappings for Chart of Accounts & unit-type labels with HITL review Parsing pipeline optimized for sub-10s total latency 5–7 yrs ML engineering (modeling, evaluation, MLOps) 3+ yrs LLM / Document AI (fine-tuning, embeddings, RAG) Hands-on with Azure AI Foundry / Azure ML / OpenAI API Python + PyTorch / Transformers expertise Experience with pgvector or AI Search for retrieval FinTech, PropTech, or Data SaaS background a plus Fluent English ownership mindset outcome-driven 90-minute async coding / model task Technical interview Paid trial sprint (build a real parser module) EST overlap Immediate start Send your GitHub, demo, or portfolio of embedding / LLM work, plus a short note on a model you’ve taken from prototype → production.
About RealQuant
RealQuant is building the first vertical AI platform for commercial real estate — turning OMs, rent rolls, and financials into structured insights that power underwriting, reporting, and portfolio intelligence.
We’re redefining how deals move from OM → LOI in under 30 minutes by combining institutional real estate expertise with modern AI automation.
Role Overview
You’ll own the AI engine behind our document parsers, data feeds, and underwriting agent — training and fine-tuning multimodal models in Azure AI Foundry to deliver sub-10s parsing and automated deal outputs (LOIs, pitch decks, summaries, and recommended assumptions).
Tech Stack
Core AI Platform : Azure AI Foundry (multimodal)
- Azure ML (training + fine-tune)
- Azure OpenAI
Retrieval & Data : Azure AI Search (vector + hybrid)
APIs & Workflows : FastAPI inference services
Ops & Monitoring : App Insights / Monitor
What You’ll Build
Expected Outcomes
Experience
Selection Process
Compensation & Setup
Independent Contractor | Hourly DOE
Full-time remote
Long-term contract with growth potential
How to Apply
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Machine Learning Engineer • arujá, estado de são paulo, br
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