Lead AI & Data Engineer

Build the AI Intelligence Layer Powering Investment Decisions

Are you an AI architect who cares less about demos and more about durable systems?

Do you want to design AI infrastructure that directly influences capital allocation, underwriting, and portfolio strategy — not just chatbot experiments?

Are you energized by building scalable knowledge systems that unify messy, real-world financial data into production-grade intelligence?

If so, this role may be exactly what you’ve been looking for.

We are seeking a Lead AI & Data Engineer to architect and build our next-generation AI knowledge platform and data intelligence layer — powering Investment and Asset Management teams with trustworthy, scalable, and auditable AI systems.

This is not a support role.
This is a platform ownership role.

About the Organization

We are a vertically integrated real estate investment and asset management platform focused on data-driven decision-making across complex credit and real asset portfolios.

Our investment teams rely on deep analysis, structured underwriting, and real-time intelligence. As we scale, we are investing in a unified AI and data platform to enhance insight generation, accelerate workflows, and create differentiated advantage.

This role sits at the center of that transformation.

The Impact You’ll Have

As Lead AI & Data Engineer, you will:

• Architect and own the enterprise AI knowledge platform

• Design the data intelligence layer that unifies structured, semi-structured, and unstructured data

• Power LLM-driven applications for underwriting, research, and investment decision support

• Transform fragmented internal and third-party data into scalable, production-grade AI systems

• Establish technical standards for reliability, governance, and AI quality across the organization

Your work will directly influence how investment decisions are informed and executed.

What You’ll Build

AI Knowledge & Retrieval Architecture

• Design and implement Retrieval-Augmented Generation (RAG) pipelines

• Architect embedding strategies, chunking frameworks, metadata modeling, multi-stage retrieval, reranking, and grounding

• Operate vector databases including Pinecone and Cosmos DB (vector workloads)

• Optimize relevance tuning and retrieval quality metrics

Enterprise Data Engineering & Intelligence

• Build scalable ingestion and transformation pipelines using SQL / Azure SQL, Cosmos DB, and Microsoft Fabric (Lakehouse, Data Pipelines, Semantic Models)

• Integrate and operationalize third-party market, property, economic, and benchmark data

• Develop entity intelligence and feature engineering pipelines across assets, funds, and geographies

• Design graph-based intelligence layers enabling relationship-aware reasoning and hybrid querying across vector, graph, and relational systems

AI Platform & Backend Systems

• Develop backend services and middleware (Python, FastAPI, Node.js) exposing AI capabilities via secure APIs

• Enable semantic search, document intelligence, Q&A, summarization, classification, and analytical services

• Orchestrate LLM workflows using LangChain, LlamaIndex, Semantic Kernel, or custom frameworks

• Securely integrate Azure OpenAI, OpenAI, and Anthropic APIs with enterprise data sources

Machine Learning & Applied Analytics

• Apply practical machine learning techniques including classification, clustering, similarity modeling, forecasting, and anomaly detection

• Enhance investment insights and improve data quality using production-ready models

• Ensure models are explainable, auditable, and aligned with business objectives

Azure AI & Enterprise Delivery

• Lead deployment and operations across Azure (Azure OpenAI, Cosmos DB, Azure SQL, Microsoft Fabric)

• Ensure scalability, fault tolerance, performance optimization, and cost efficiency

• Establish observability, logging, CI/CD pipelines, access controls, and governance standards

• Build LLM evaluation frameworks and retrieval quality metrics

Team Leadership & Collaboration

• Provide technical leadership across AI engineering and data platforms

• Define architecture standards and reusable design patterns

• Mentor engineers and establish best practices for production AI systems

• Partner closely with Investment, Asset Management, Research, and Technology teams to translate business problems into scalable AI solutions

• Balance engineering rigor with speed-to-value

What Will Help You Succeed

Education & Experience

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field

• 7+ years of experience across AI engineering, data engineering, backend development, or applied machine learning

• At least 2 years in a senior or technical leadership capacity

• Proven experience building RAG-based systems and production AI platforms

• Experience working with investment, financial, or real asset data strongly preferred

Technical Expertise

• Deep experience integrating LLM APIs (Azure OpenAI, OpenAI, Anthropic Claude)

• Strong command of embeddings, retrieval strategies, and orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel)

• Expertise in vector databases (Pinecone, Cosmos DB vector), relational systems (Azure SQL), graph modeling, and Microsoft Fabric

• Advanced proficiency in Python (FastAPI) and Node.js for scalable API development

• Azure-first cloud deployment and DevOps experience including CI/CD, observability, and governance

Professional Strengths

• Strong analytical mindset with the ability to connect technical systems to business impact

• Clear communicator capable of translating complex AI architecture into business value

• High ownership mentality and bias toward execution

• Comfort operating in evolving, high-ambiguity environments

Working Conditions

This role may require occasional standing, walking, lifting up to 25 pounds, and occasional evening or weekend work to meet deadlines. This description outlines the primary responsibilities and requirements but is not an exhaustive list of duties.

Equal Employment Opportunity

We are committed to providing equal employment opportunity to all applicants and employees without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity or expression, national origin, age (40+), disability, veteran status, genetic information, or any other legally protected status.