Senior AI Engineer at MLG Capital

Goerke's Corners, Wisconsin, United States -

Full Time

Start Date

Immediate

Expiry Date

02 Aug, 26

Salary

0.0

Posted On

05 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Azure AI Foundry, Azure OpenAI Service, Python, RAG, Agentic Systems, Microsoft Fabric, Microsoft Purview, Microsoft Graph, Microsoft Entra ID, CI/CD, Infrastructure-as-Code, Vector Databases, LLM Evaluation, System Design, Cloud-native Development, API Integration

Industry

Investment Management

Description

Description About MLG Capital MLG Capital is a private real estate investment manager that has been operating since 1987. The firm is focused on delivering 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, not a set of experiments. 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. Role Overview 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 (Azure AI Foundry / Azure OpenAI Service, Azure Machine Learning where applicable, Microsoft Fabric, Microsoft Purview, Microsoft Graph, and Microsoft Entra ID). This is not a low/vibe-code automation role. Instead, you will build and operationalize a secure, governed, Azure-hosted AI platform layer—supporting LLM/RAG and agentic patterns—integrated with enterprise identity, data, and observability standards. Org-wide AI architectures Agentic systems and orchestration Secure AI + data integrations using Microsoft-native services (Entra ID, Purview, Fabric/OneLake, Graph, and Azure networking patterns) Production deployment, evaluation, and governance of LLM-based systems You will partner closely with the Organizations Operating Committee, SVP Data, Data Engineering, BI, Security, Legal/Compliance, and business users to move the current AI roadmap from ideas to pilots to durable enterprise infrastructure. Core Responsibilities Enterprise AI Platform Architecture 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. AI-Ready Data & Context Engineering 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. Workflow Automation ? Predictive ? Autonomous Systems Build AI systems that move beyond automation into: Intelligence-assisted workflows Predictive insights and early-warning signals 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. Agentic Systems & Orchestration Architect and implement agent-based systems capable of: Multi-step reasoning Tool invocation across enterprise systems 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. Governance, Security & Permissioning by Design 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 Responsible AI principles Design AI systems where what the user can ask and what the system can do are explicitly governed, not implied. Platform Observability, Evaluation & ROI Discipline Implement enterprise evaluation and monitoring frameworks (offline evals + online monitoring) using Azure-native observability (Azure Monitor / Application Insights / Log Analytics) to measure: Accuracy, groundedness, and reasoning quality Drift, latency, and reliability over time Cost, usage, and adoption patterns Accuracy and reasoning quality Drift and reliability over time 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. Partnership-Driven Delivery 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. Requirements 4+ years AI/software engineering experience, with 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 Scalability Deep understanding of modern LLMs and tradeoffs across model providers Advanced proficiency in: Python APIs Cloud-native development (Azure preferred) Experience integrating AI with enterprise data, workflows, and systems, not just standalone apps Preferred / Differentiating Experience Hands-on experience with: Azure AI Foundry / Azure OpenAI Service (plus Azure AI Search and Azure Machine Learning as needed) Microsoft Fabric/ Microsoft Purview 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 Evaluation and tracing tools for LLM systems Experience operating AI in regulated or compliance-sensitive environments Mindset & Working Style Builder mentality with strong system design instincts Comfortable operating in ambiguity while driving towards production outcomes High ownership, able to move from concept to deployed system Strong collaborator across technical and business teams Pragmatic, security-minded, and cost-aware Additional Notes: Physical Requirements: Ability to operate office machinery; including but not limited to: telephone, computer, copy machine, fax machine, printer, and mobile phone. Ability to sit for extended periods (up to 4 hours) and use a computer for up to 8 hours per day. Ability to lift up to 10 pounds on an occasional basis. Working Conditions: Open office workstation environment, quiet to moderate noise levels. SEC Compliance: As MLG has a subsidiary Registered Investment Adviser, many employees are subject to SEC-mandated compliance requirements. As part of these requirements, employees must disclose personal brokerage accounts and financial holdings, for themselves and any household members whose investment activities they influence. This information is collected solely for regulatory compliance and conflict of interest monitoring. All disclosures are handled with strict confidentiality and are accessible only to the Chief Compliance Officer and designated compliance personnel when a business or SEC related need arises All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, disability, sexual orientation, national origin or any other category protected by law. In compliance with the Americans with Disabilities Act, a “reasonable accommodation” will be made for an individual with a known physical or mental limitation unless it would require an action of significant difficult causing undue hardship. This document covers the most significant duties performed but does not exclude other occasional work assignments not mentioned.

Responsibilities

Lead the technical design and scaling of a secure, governed enterprise AI platform on Azure to support LLM and agentic patterns. Partner with cross-functional teams to integrate AI capabilities across investment, finance, and operations while ensuring strict governance and observability.