Senior AI Engineer

MLG Capital • Goerke's Corners, WI

1h • Onsite

About The Position

MLG Capital is a private real estate investment manager founded in 1987, focusing on long-term, tax-efficient, risk-adjusted returns through diversified real estate strategies across the U.S. As the firm scales, AI is becoming a core platform capability. This role is critical to building production-grade AI systems that operate securely, governably, and at enterprise scale across investment, asset management, investor operations, finance, and marketing. We are seeking a Senior AI Engineer to lead the technical design, development, and scaling of enterprise AI systems across MLG Capital, with core experience anchored in the Microsoft AI stack. This role involves building and operationalizing a secure, governed, Azure-hosted AI platform layer supporting LLM/RAG and agentic patterns, integrated with enterprise identity, data, and observability standards. You will partner closely with various leadership and business teams to advance the AI roadmap from ideas to pilots to durable enterprise infrastructure.

Requirements

• 4+ years AI/software engineering experience
• 2+ years building and operating production AI / LLM systems at enterprise scale (multi-environment deployments, CI/CD, observability, and security controls)
• Strong experience deploying AI systems that handle concurrency, failure modes, observability, and scalability
• Deep understanding of modern LLMs and tradeoffs across model providers
• Advanced proficiency in Python
• Advanced proficiency in APIs
• Advanced proficiency in Cloud-native development (Azure preferred)
• Experience integrating AI with enterprise data, workflows, and systems, not just standalone apps

Nice To Haves

• Hands-on experience with Azure AI Foundry / Azure OpenAI Service (plus Azure AI Search and Azure Machine Learning as needed)
• Hands-on experience with Microsoft Fabric/ Microsoft Purview
• Hands-on experience with Microsoft Graph APIs
• Experience with agent frameworks
• Strong intuition for when not to use AI, and how to blend deterministic systems with probabilistic reasoning
• Familiarity with vector databases and hybrid search
• Familiarity with evaluation and tracing tools for LLM systems
• Experience operating AI in regulated or compliance-sensitive environments

Responsibilities

• Design and evolve MLG’s enterprise AI platform layer on Azure that connects models, data, tools, and permissions into a secure, scalable system of intelligence (RBAC/ABAC via Microsoft Entra ID, network isolation, and auditability).
• Build foundational patterns for retrieval-augmented generation (RAG) and agentic workflows that enable natural-language interaction over governed enterprise data (Fabric/OneLake, SharePoint/Teams content via Microsoft Graph) with lineage and classification enforced through Microsoft Purview.
• Establish architectural standards that allow AI capabilities to compound over time (reusable services, shared prompt/context patterns, and repeatable deployment via infrastructure-as-code and CI/CD across dev/test/prod), rather than exist as isolated point solutions.
• Partner with Data Engineering to ensure centralized, AI-ready data foundations in the Microsoft data estate (Fabric/OneLake, lakehouse/warehouse patterns), including structured, semi-structured, and unstructured data.
• Implement robust retrieval, context assembly, and permission-aware access strategies (Entra ID–backed authorization and Purview-aligned governance) so AI systems return accurate, explainable, and compliant outputs.
• Design systems that blend internal performance data, historical decisions, and market intelligence into unified AI context pipelines.
• Build AI systems that move beyond automation into intelligence-assisted workflows, predictive insights and early-warning signals, and semi-autonomous execution with human-in-the-loop controls.
• Design architectures that support progressive maturity, allowing workflows to evolve from copilots to decision-support engines and, where appropriate, autonomous agents.
• Architect and implement agent-based systems capable of multi-step reasoning, tool invocation across enterprise systems, and coordinated execution across specialized agents.
• Balance agent autonomy with deterministic controls, cost ceilings, and auditability to ensure enterprise reliability and trust.
• Establish reusable agent frameworks that can be extended across acquisitions, asset management, portfolio management, investor operations, and finance.
• Embed model, context, and permission controls directly into AI system architecture (Entra ID-based authentication/authorization, policy enforcement, and end-to-end audit logging).
• Partner with Compliance, Legal, and Security to ensure AI systems respect data classification and access controls, regulatory and SEC-aligned constraints, and Responsible AI principles.
• Design AI systems where what the user can ask and what the system can do are explicitly governed, not implied.
• Implement enterprise evaluation and monitoring frameworks (offline evals + online monitoring) using Azure-native observability (Azure Monitor / Application Insights / Log Analytics) to measure accuracy, groundedness, reasoning quality, drift, latency, reliability, cost, usage, and adoption patterns.
• Support leadership in understanding where AI delivers durable ROI versus aspirational or experimental value.
• Ensure AI systems are observable, debuggable, and measurable as enterprise platforms—not black boxes.
• Operate within MLG’s hub-and-spoke AI model, acting as the central technical owner of AI frameworks while partnering deeply with business lines.
• Work alongside internal teams and external partners, ensuring all solutions integrate cleanly into MLG’s Microsoft-centric ecosystem.
• Help translate business intent into repeatable technical primitives, enabling scale without bespoke engineering per use case.

Benefits

• Consideration for employment without regard to race, color, religion, sex, age, disability, sexual orientation, national origin or any other category protected by law.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed