AI Integration Architect

CoreLogic • Austin, TX

1d • $134,400 - $200,000 • Hybrid

About The Position

At Cotality, we are driven by a single mission—to make the property industry faster, smarter, and more people-centric. Cotality is the trusted source for property intelligence, with unmatched precision, depth, breadth, and insights across the entire ecosystem. Our talented team of 5,000 employees globally uses our network, scale, connectivity and technology to drive the largest asset class in the world. Join us as we work toward our vision of fueling a thriving global property ecosystem and a more resilient society.
Cotality is committed to cultivating a diverse and inclusive work culture that inspires innovation and bold thinking; it's a place where you can collaborate, feel valued, develop skills and directly impact the real estate economy. We know our people are our greatest asset. At Cotality, you can be yourself, lift people up and make an impact. By putting clients first and continuously innovating, we're working together to set the pace for unlocking new possibilities that better serve the property industry.
About Cotality
Cotality is the insurance industry's leading provider of property intelligence, risk analytics, and workflow solutions. Our data powers underwriting, claims, and catastrophe risk decisions for the nation's largest carriers. We are building the AI-first data foundation that defines how the industry consumes property intelligence, and this role sits at the center of that transformation.
Role Summary
We are looking for a Senior AI Architect to design and deliver AI systems across Cotality's property intelligence platform. This is a hands-on individual contributor role with broad scope spanning internal agentic systems that power property analytics and decision workflows, and external AI integration architecture that makes Cotality's data products consumable by AI agents, foundation model platforms, and enterprise developer ecosystems.
You will work across both layers, ensuring they are coherent, secure, and built to scale with the growth of our product portfolio. You will define the standards, build the foundational components, and be accountable for the architecture working in production under real client load.

Requirements

• 7 to 10 years of software engineering or architecture experience, with a strong background in API platforms and distributed systems, and recent proven depth building LLM-powered or agentic AI applications.
• Strong backend development background in Python, Java, or .NET, with an expert understanding of object-oriented design and distributed microservices. As the AI orchestration ecosystem is heavily Python-centric, candidates must be proficient in Python or demonstrably capable of transitioning into it for the agentic and tooling layers.
• Hands-on experience with agentic AI frameworks such as LangChain, LlamaIndex, or AutoGen, with strong command of prompt engineering, context management, and tool integration.
• Solid foundation in API architecture and authentication patterns, including REST, OAuth 2.0, JWT design, and API gateway technologies such as Apigee or Kong, with an understanding of how security is enforced across service boundaries.
• Working knowledge of Agent Experience (AX) design principles including context window constraints, prompt drift, tool description quality, and the failure modes that emerge when API schemas are ambiguous or inconsistent to an LLM.
• Familiarity with AI observability tooling and the operational differences between monitoring traditional software systems and monitoring non-deterministic LLM systems.
• Experience with CI/CD practices and a working understanding of how to apply evaluation and testing frameworks to non-deterministic AI systems.
• Clear, precise written communication with the ability to produce technical documentation that non-specialist stakeholders can act on.

Nice To Haves

• Experience with MCP (Model Context Protocol), LiteLLM, or LLM gateway and proxy patterns.
• Familiarity with Snowflake, Databricks, or GCP.
• Hands-on experience with containerized deployment environments including Docker, Kubernetes, and Terraform or equivalent IaC tooling.
• Strong understanding of AI infrastructure patterns, including Kubernetes GPU scheduling with taints, tolerations, and NVIDIA Device Plugins, Terraform for heterogeneous node pools, and containerization best practices for CUDA or ROCm (Radeon Open Ecosystems) environments.
• Background in regulated industries such as insurance, financial services, or healthcare, where data provenance, audit logging, and compliance-oriented architecture are part of the design requirements.
• Experience with multi-cloud architectures and cross-cloud connectivity patterns.
• Exposure to developer experience tooling API documentation platforms, sandbox environments, or SDK design

Responsibilities

• Design and build agentic AI systems, including multi-agent frameworks, orchestration layers, memory and retrieval architectures, and tool-based reasoning pipelines that operate against structured and unstructured property data.
• Own the external AI integration architecture, API gateway configuration, MCP server patterns, authentication and authorization flows, tool schema standards, and the reference architecture that product teams follow to expose their APIs as agent-consumable tools.
• Pioneer Agent Experience (AX) design as a first-class methodology for the organization analogous to UX or Developer Experience (DX). Treat AI agents as primary consumers of our systems and ensure that APIs, tool descriptions, and data outputs are optimized for LLM comprehension, context limits, and deterministic reasoning.
• Establish and enforce technical standards for how AI agents consume Cotality's data products, with a focus on tool description quality, input and output contracts, error handling patterns, and response metadata standards all evaluated through the lens of AX.
• Architect security and data provenance controls across the integration layer, including JWT claim schema design, defense-in-depth authorization patterns, audit logging, and response boundary enforcement.
• Design and implement observability and telemetry for AI systems to monitor token consumption, latency, error rates, prompt drift, LLM costs, and response quality in production.
• Establish CI/CD pipelines and evaluation frameworks for AI agents that measure accuracy, hallucination rates, and performance regressions before changes reach production.
• Optimize AI workload architecture by designing deployment strategies that decouple large model weights from application code, utilizing optimized base images and dynamic runtime mounting to maintain fast, reliable CI/CD pipelines.
• Scale inference and orchestration by architecting high-throughput AI backends using specialized model servers such as vLLM or Triton on Kubernetes, with support for dynamic batching, streaming responses, and concurrent execution.
• Align application design with cloud economics by partnering with platform engineering to build cost-aware AI systems, and designing agentic workflows that gracefully handle cold-start latencies and infrastructure scaling events such as scale-to-zero or Spot instance evictions without dropping requests.
• Bring strong backend engineering practices to the AI layer, with a consistent track record of delivering production-quality, maintainable code in cloud or containerized environments.
• Define the reference architecture and MCP server build patterns that product teams across the organization follow when exposing their APIs as agent-consumable tools.
• Partner closely with data engineers, product managers, and domain experts in insurance and property risk to ensure that AI systems produce outputs that are accurate, traceable, and operationally meaningful.
• Review implementations, conduct architecture design reviews, and hold the technical quality bar as the connector portfolio grows and more teams contribute.
• Produce architecture decision records, technical standards, and reference documentation that engineering teams across the organization rely on to make consistent, well-reasoned decisions.

Benefits

• Generous PTO and 11 paid holidays, plus well-being and volunteer time off.
• Up to 16 weeks of fully paid parental leave and a baby stipend.
• Multiple medical plan options with mental health and wellness support offerings.
• 401(k) with company match and vesting after one year.
• $400 annual well-being stipend and tuition assistance up to $5,250.
• Recognition Rewards, Referral bonuses, exclusive discounts and more!

What This Job Offers

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