Role Description The AI Engineer is the builder-in-residence for Camden's AI Sandbox, the dedicated environment where we rapidly prototype, evaluate, and de-risk emerging AI capabilities before they enter production. This role exists to move fast: turning business ideas into working proofs-of-concept, testing new models and tools, and producing the evidence that helps Camden decide what to scale, what to shelve, and what to integrate into core systems. This is a high-autonomy, high-curiosity role for someone who thrives in ambiguity and enjoys learning new technologies week after week and sharing what they find with others. Essential Functions - Rapid Prototyping & Experimentation: - Design, develop, and deploy functional prototypes of AI-driven solutions within accelerated timelines, spanning use cases such as document understanding, generative conversational interfaces, agent-based workflows, summarization, enterprise search, and predictive analytics. - Evaluate new foundation models, frameworks (LangChain, Semantic Kernel, AutoGen, etc.), and AI platforms (Azure AI Foundry, OpenAI, Anthropic, Copilot Studio) for fit against Camden's use cases. - Maintain a safe, isolated sandbox environment with appropriate guardrails for data, cost, and access. - This role is expected to evolve over time as organizational needs and priorities change; therefore, the responsibilities outlined above are not all-inclusive. - Evaluation & Measurement: - Define success criteria, benchmarks, and evaluation harnesses for each experiment. - Produce clear, evidence-based recommendations: what works, what doesn't, what it would cost to scale, and what risks need to be mitigated. - Document key insights and lessons learned to enable broader team and business stakeholders to effectively leverage and scale these capabilities. - Collaboration & Handoff: - Partner with the Application Development team to harden and transition promising prototypes into production-grade systems. - Work directly with business stakeholders to understand problems, demo possibilities, and shape demand. - Contribute to the development and advancement of Camden’s responsible AI practices, with a focus on data privacy, security, bias mitigation, hallucination management, and human-in-the-loop design principles. - Knowledge Sharing: - Run internal demos, lunch-and-learns, and writeups that raise AI literacy across IT and business. - Stay current on the AI landscape; bring back insights from research, vendor briefings, and the developer community. Qualifications - Bachelor's degree in Computer Science, Data Science, Engineering, or related field; equivalent experience considered. - 3+ years of software engineering or applied ML experience, with at least 1 year hands-on with generative AI / LLM-based systems. - Strong Python skills; comfort with REST APIs, vector databases, prompt engineering, and retrieval-augmented generation (RAG). - Experience with at least one major AI platform (Azure OpenAI / Azure AI Foundry, OpenAI, Anthropic, AWS Bedrock). - Demonstrated ability to ship prototypes quickly and communicate results to non-technical audiences. - Excellent written and verbal communication skills; ability to translate between technical and business audiences. - Preferred experience with agent frameworks, multi-agent orchestration, and tool-use patterns. - Preferred experience with Microsoft Copilot Studio, Power Platform AI Builder, or similar low-code AI tooling. - Preferred familiarity with MLOps / LLMOps practices (evaluation, observability, cost tracking). - Preferred background in real estate, property management, or other operations-heavy industries. - Preferred comfort working without a detailed spec — turning a vague business question into a testable experiment. Requirements - Job is intermittently sedentary but requires mobility (i.e., climb stairs). - Will use some repetitive motion of hand-wrist in using computer and writing. - Works in a typical office setting. - Emotional stability and personal maturity are important attributes in this position. - Must handle stressful, urgent, novel and diverse work situations on a daily basis. - May require long hours and odd schedules (including weekends). - Position requires periodic travel by automobile to handle work-related activities. - May require airline travel, out-of-town and/or overnight trips. - Attendance and punctuality are essential for success in this position. - Hazards can be minimized with proper lifting techniques, SDS, general safety training, and wearing appropriate PPE. Benefits To learn more about our awesome Benefits, visit Camden Benefits.

• Build AI-powered product features integrated into real insurance workflows. • Design and optimize LLM-based interactions across customer and internal systems. • Develop prompts, reasoning flows, and structured AI outputs for production use cases. • Evaluate model performance using real-world data, feedback loops, and experiments. • Improve reliability of AI outputs through guardrails, fallback logic, and validation layers. • Work closely with backend engineers to integrate AI capabilities into APIs and workflows. • Design and implement AI-driven automation features (chat, recommendations, decision support). • Analyze user behavior and model responses to continuously improve AI performance. • Collaborate with product, design, and engineering teams to ship end-to-end AI features. • Contribute to AI system design decisions focused on usability and real-world impact.

• Lead the design and development of production AI systems powering insurance workflow automation • Architect AI orchestration layers connecting LLMs, backend services, and business workflows • Own end-to-end AI system design, including inference pipelines, routing, caching, and fallback strategies • Drive engineering decisions around latency, reliability, cost, and scalability of AI services • Lead implementation of Observability Systems (logging, monitoring, tracing, alerting) • Review and guide backend AI implementation across engineering teams • Collaborate with product, backend, DevOps, and operations teams to ship end-to-end AI features • Debug complex production issues across distributed AI systems and lead root-cause analysis • Define engineering standards and best practices for AI system development • Mentor engineers and elevate technical execution quality across teams

• Build AI-powered product features integrated into real insurance workflows. • Design and optimize LLM-based interactions across customer and internal systems. • Develop prompts, reasoning flows, and structured AI outputs for production use cases. • Evaluate model performance using real-world data, feedback loops, and experiments. • Improve reliability of AI outputs through guardrails, fallback logic, and validation layers. • Work closely with backend engineers to integrate AI capabilities into APIs and workflows. • Design and implement AI-driven automation features (chat, recommendations, decision support). • Analyze user behavior and model responses to continuously improve AI performance. • Collaborate with product, design, and engineering teams to ship end-to-end AI features. • Contribute to AI system design decisions focused on usability and real-world impact.