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MRI Software

Real estate management and lease software

Director of AI Engineering - AI

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • At least 10 years of engineering leadership experience, including hiring, organizational design, and developing engineering talent while scaling multi-team organizations.
  • Deep hands-on experience architecting and delivering AI and machine-learning systems, including large language model-based applications, retrieval-augmented generation architectures, and agentic AI patterns.
  • Advanced Python skills sufficient to participate directly in architecture reviews and hands-on technical problem-solving.
  • Experience with Model Context Protocol or comparable interoperability and integration frameworks for AI systems.
  • Strong background in API design, microservices, and modern cloud-native architectures such as Azure, AWS, or GCP.
  • Experience improving engineering delivery velocity through tooling, processes, and AI-assisted development practices.
  • Working familiarity with product lifecycle management practices and ability to partner effectively with product management without directly owning the product roadmap.
  • Strong understanding of data pipelines, data platforms, and the data foundations required for AI at scale.
  • Grounding in data science concepts and principles, including statistical reasoning, model evaluation, and data quality practices.
  • Experience establishing AI governance, model risk management, and responsible AI practices.
  • Technical judgment in high-scalability, high-performance system design.
  • Ability to operate and influence at the executive level across Engineering, Product, and business stakeholders.
  • Experience owning infrastructure and vendor cost accountability, including cost monitoring, unit economics, and return-on-investment measurement for technology investments.
  • Experience establishing security practices for AI and agentic systems, including threat modeling and identity and access patterns for automated systems.
  • A Bachelor's degree in Computer Science, Engineering, or a related technical field is required.

Responsibilities

  • Define and drive the AI engineering roadmap, including build-versus-buy decisions, platform architecture, and technology direction.
  • Lead and scale the AI engineering organization responsible for model integration, retrieval-augmented generation systems, agent orchestration, and AI platform services, including hiring, organizational design, and coaching engineering leaders and architects.
  • Own the shared AI platform, including prompt management, evaluation frameworks, vector databases, model gateway and routing services, and agent registries.
  • Champion adoption of Model Context Protocol and other emerging interoperability standards to connect AI capabilities across the product ecosystem.
  • Define the enterprise agent ecosystem approach, including agent interoperability, shared memory and context management, cross-product orchestration, and reusable agent capabilities across the portfolio.
  • Architect and scale agentic AI ecosystems operating across multiple products, data sources, and internal systems.
  • Define AI reference architectures and reusable patterns for the AI engineering organization, driving standardization over product-level fragmentation.
  • Partner with product leadership to translate AI roadmap priorities into engineering execution and sequencing.
  • Establish engineering practices, tooling, and governance that increase delivery velocity for AI-powered features across the organization.
  • Drive AI-assisted development practices, including AI-assisted coding and workflow tooling, to improve engineering team productivity.
  • Own model lifecycle management from evaluation and selection through deployment, monitoring, and retirement.
  • Select suitable models and AI technologies for use cases, balancing cost, quality, and scale tradeoffs.
  • Establish AIOps and LLMOps practices, including observability and telemetry, hallucination detection, and continuous evaluation and benchmarking of models and agents.
  • Own AI infrastructure and model consumption costs, establish unit economics and return-on-investment measurement for AI features, and drive FinOps discipline across the AI platform.
  • Set standards for API design and integration patterns that support AI feature delivery across platforms.
  • Own the knowledge architecture underlying AI systems, including semantic layers, metadata and context management, data governance for AI, and knowledge graph design.
  • Partner with data engineering and platform teams to ensure the data foundation supports AI initiatives at scale.
  • Evaluate and pilot emerging AI technologies, translating industry trends into practical initiatives.
  • Establish governance and responsible AI practices for model usage, data handling, and agent behavior, including AI and agent security, threat modeling, prompt injection defense, data leakage prevention, and identity and authorization patterns for agents.
  • Engage directly with customers and executive leadership on AI trust, adoption, and business impact.

Desired Qualifications

  • A technical graduate degree is preferred.
  • Background leading AI engineering in a SaaS or enterprise software environment.
  • Experience leading globally distributed engineering teams across multiple regions.
  • A track record of leading engineering organizations through significant technical or organizational transformation.
  • Comfort running vendor evaluations and build-versus-buy assessments for AI platforms and tooling.
  • Experience integrating AI capabilities into acquired products or teams following an acquisition.

About the Company

MRI Software

Real estate management and lease software

MRI Software delivers real estate technology by offering a suite of open, connected software tools for real estate owners, operators, and occupiers. It provides property management, lease management, and digital services designed to integrate with clients’ existing systems. The product portfolio is accessed through a subscription model and can be extended via a partner ecosystem, along with professional and support services. The tools help clients manage tenant communications, financial reporting, and day-to-day operations from a single platform. The company differentiates itself by offering an integrated, globally deployed software suite and an ecosystem that enables customization and seamless integration rather than standalone point solutions. The goal is to help clients streamline property operations, improve efficiency, and drive value across the real estate market.

Company size
1,001-5,000
Company stage
Debt Financing
Total funding
$19.5M
Headquarters
Solon, OH
Founded
1971

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