Senior AI Platform Engineer

eLEND is modernizing mortgage using a cloud-first, AI-driven approach to create smarter home financing experiences. As the first Senior AI Platform Engineer, you will take ownership of the agentic AI platform, driving it from proof-of-concept to an enterprise-grade production system while defining architecture and engineering practices for AI workloads.

Responsibilities:

• Assess and extend our existing Semantic Kernel (C#) and Microsoft 365 Agents SDK implementation and define target-state architecture
• Design and implement multi-agent orchestration pipelines, including planning, memory/state management, tool usage, routing, and inter-agent communication
• Establish engineering standards for agent lifecycle management, including registration, discovery, monitoring, and scaling
• Implement guardrails, safety controls, fallback logic, and human-in-the-loop escalation workflows
• Build intelligent agents supporting lending workflows such as document intake, underwriting decision support, compliance validation, and customer communication
• Expand Azure AI Foundry integration for model deployment, prompt lifecycle management, and evaluation pipelines
• Integrate Azure OpenAI models (GPT-4o, GPT-4 Turbo, o-series, embeddings) into agent workflows with focus on performance, quality, and cost optimization
• Build and optimize Retrieval Augmented Generation (RAG) pipelines using Azure AI Search, vector databases, and hybrid retrieval strategies
• Design Graph-based RAG architectures to support regulatory knowledge graphs, compliance validation, and entity resolution
• Evaluate and implement models for document understanding, classification, summarization, and conversational interfaces
• Develop production pipelines using Azure AI Document Intelligence to extract, classify, and validate lending documents
• Design orchestration workflows combining document intelligence and LLM reasoning for multi-document analysis
• Expand Microsoft Fabric integration to support unified data lakehouse architecture for AI workloads
• Build embedding pipelines, feature stores, and data preparation workflows to support intelligent agent decisioning
• Implement telemetry, monitoring, logging, and observability for AI performance, cost, and reliability
• Implement Microsoft Purview for data governance, lineage tracking, and sensitivity labeling
• Design and enforce content safety policies using Azure AI Content Safety
• Build AI guardrails including hallucination detection, grounding validation, prompt injection defense, and output filtering
• Establish model governance processes including versioning, evaluation, A/B testing, rollback, and audit trails
• Ensure AI platform compliance with lending and financial regulations including ECOA, FCRA, TILA, RESPA, and fair lending requirements
• Define engineering patterns, architecture standards, and best practices for AI platform development
• Contribute to hiring strategy, platform roadmap, and long-term AI engineering maturity
• Collaborate cross-functionally with engineering, compliance, data, and business stakeholders
• Contribute hands-on engineering across platform components, infrastructure, and orchestration workflows

Requirements:

• 7+ years of software engineering experience, including 2+ years building AI/ML or LLM-based systems
• Hands-on experience building production AI platforms, agent orchestration systems, or multi-agent workflows
• Experience with agent frameworks such as Semantic Kernel, Microsoft 365 Agents SDK, LangChain, LangGraph, AutoGen, or CrewAI
• Strong experience with Azure OpenAI, Azure AI Foundry, Azure AI Search, or equivalent cloud AI platforms
• Production experience designing and implementing RAG pipelines and vector-based retrieval systems
• Strong programming skills in C#/.NET and/or Python
• Experience implementing AI guardrails, safety controls, and responsible AI practices
• Strong architectural thinking and ability to move systems from POC to production
• Experience in mortgage, lending, fintech, or regulated financial environments
• Experience building Graph RAG or knowledge graph-driven AI systems
• Familiarity with Microsoft Fabric, Purview, and enterprise data platforms
• Experience with prompt engineering platforms, evaluation frameworks, or model lifecycle management
• Experience implementing MLOps pipelines, model monitoring, and drift detection
• Experience with agent-assisted development tools such as GitHub Copilot, Codex, or Cursor