About
Lennar Technology Group (LTG) is building enterprise AI capabilities that directly accelerate the nation's largest homebuilder. We are looking for a Principal Machine Learning Engineer / Senior Software Developer who brings deep expertise at the intersection of financial technologies and applied artificial intelligence to join our Applied AI & Solutions Design team. In this role, you will architect and deliver production-grade ML systems that serve Finance, Accounting, Capital Markets, and Mortgage operationsworking shoulder-to-shoulder with Senior Enterprise Architects, Senior Business Solutions Architects, and cross-functional stakeholders across the organization. You will regularly present strategy, progress, and technical recommendations to SVP-level and C-suite leadership, making this a high-visibility position with material impact on corporate decision-making. The ideal candidate will combine strong ML/AI engineering skills with a proven track record in financial services, fintech, or corporate finance environmentsand will bring the executive communication skills required to translate complex technical work into business outcomes for senior leadership audiences. Your Responsibilities On The Team
Design, build, and deploy production ML models and pipelines for financial applications including forecasting (home starts, revenue, margin), anomaly detection, risk scoring, and portfolio analytics. Develop AI-driven solutions that integrate with core financial systems (ERP, GL, treasury, mortgage origination) to automate and enhance decision-making. Architect data pipelines and feature stores that ingest structured financial data (transactions, P&L, balance sheet, loan-level tapes) for model training and inference at enterprise scale. Implement model governance, versioning, explainability (SHAP, LIME), and auditability frameworks to satisfy internal compliance and regulatory requirements. Contribute to LTG's enterprise AI platform roadmap spanning GenAI observability, Agent Registry, LLM model deployment, and AI governance. Build and operate multi-agent systems (AWS Bedrock, Strands/AgentCore, Anthropic APIs) integrated with enterprise tools (ServiceNow, Confluence, SharePoint, Microsoft Entra). Partner with Senior Enterprise Architects and Business Solutions Architects to ensure ML solutions align with Lennar's technology standards, security posture, and cloud architecture. Evaluate and integrate third-party AI/ML platforms and observability tooling (e.g., Weights & Biases, Coralogix) into the production stack. Present technical strategy, project outcomes, and investment recommendations to SVP and C-suite leadership on a regular cadence. Translate complex ML and financial modeling concepts into clear, actionable business narratives for non-technical executives. Represent the Applied AI team in cross-functional steering committees, budget reviews, and strategic planning sessions with Finance, HR, IT, and Operations leadership. Author executive-ready documentation including business cases, architecture decision records, and compliance impact assessments. Work closely with Finance, Accounting, Mortgage, and Capital Markets teams to identify high-value ML use cases and translate business requirements into technical specifications. Partner with the Technology Compliance team on AI governance, data privacy (PII handling in LLM traces), and regulatory alignment. Mentor engineers and analysts on ML best practices, financial domain knowledge, and production engineering standards. What You Bring Required Qualifications
8+ years of professional software engineering or ML engineering experience, with at least 4 years focused on financial services, fintech, capital markets, mortgage technology, or corporate finance applications. Deep working knowledge of financial data structures, instruments, and processes (e.g., loan origination, underwriting models, financial forecasting, treasury operations, regulatory reporting). Demonstrated experience building ML models that consume financial data to drive business outcomes (e.g., credit risk, pricing optimization, fraud detection, demand forecasting, margin analysis). Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost) with production deployment experience. Hands-on experience with LLMs, RAG architectures, vector databases, and agentic AI patterns (multi-agent orchestration, tool use, function calling). Strong background in cloud-native ML infrastructure (AWS SageMaker, Bedrock, Lambda, Step Functions; or Azure ML, OpenAI Service). Experience with ML observability, experiment tracking, model registry, and CI/CD for ML pipelines (MLflow, W&B, Kubeflow, or equivalent). Proven track record of presenting technical work to SVP-level or C-suite audiences in prior roles; comfort operating in high-visibility, executive-facing settings. Ability to author polished executive summaries, business cases, and strategy documents that connect technical capabilities to business value. Experience reporting directly to or partnering closely with senior leadership (VP / SVP / C-suite) on technology strategy and investment decisions. Bachelor's degree in Computer Science, Financial Engineering, Applied Mathematics, Statistics, Economics, or a related quantitative discipline. Master's degree or MBA with a quantitative or finance concentration is strongly preferred. Equivalent experience in financial technology or quantitative finance will be considered in lieu of advanced degree. What Sets You Apart Preferred Qualifications
Prior experience in homebuilding, real estate development, construction technology, property management, mortgage lending, or title/escrow operations. Familiarity with homebuilder business processes: land acquisition, entitlements, starts planning, purchasing, construction scheduling, closings, and warranty. Understanding of construction ERP systems (e.g., JD Edwards, SAP, BuildPro, Hyphen Solutions) and how financial data flows through homebuilding operations. Experience with real estate data platforms, MLS integrations, or housing market analytics. Experience with Model Context Protocol (MCP) integration, AWS AgentCore, or similar agentic AI orchestration frameworks. Background in NLP applied to document-heavy financial workflows (contracts, disclosures, compliance documents, call transcripts). Familiarity with AI governance frameworks, algorithmic bias assessment, and emerging AI regulations (Colorado AI Act, NYC Local Law 144, EU AI Act). Published research or patents in financial ML, NLP, or related fields. Experience standing up AI/ML functions or Centers of Excellence within large enterprises. History of vendor evaluation and platform selection for enterprise AI/ML tooling. Board-level, investor, or executive committee presentation experience. Technology Environment
You will work across the following technology landscape (not all-inclusive): Category Technologies Cloud & Infra AWS (Bedrock, SageMaker, Lambda, Step Functions, AgentCore, PrivateLink), Azure OpenAI Service ML / AI Python, PyTorch, TensorFlow, scikit-learn, XGBoost, LangChain, LlamaIndex, Anthropic APIs Data & Analytics Snowflake, Redshift, dbt, Spark, Pandas, SQL, feature stores Agentic AI Multi-agent orchestration (Strands/AgentCore), RAG, vector DBs (Pinecone, pgvector), MCP Observability Weights & Biases Weave, Coralogix, MLflow, CloudWatch Enterprise Systems ServiceNow, Confluence, SharePoint, Microsoft Entra, JD Edwards DevOps / MLOps Terraform, Docker, GitHub Actions, CI/CD, GitOps Why Lennar Technology Group
Be part of the AI transformation at one of America's largest homebuilders with $35B+ in annual revenue. Join a small, high-impact Applied AI team with direct access to executive leadership and strategic decision-making. Work on production AI systems that affect real business outcomesnot just prototypes. Opportunity to shape AI governance, compliance, and platform standards at enterprise scale. Collaborative culture that values technical depth, intellectual
Languages
- English
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