Role Description We are seeking an Engineering Subject Matter Expert to help develop next-generation AI systems by applying real-world engineering expertise to evaluate, create, and improve technical content. In this role, you will review engineering documentation, assess technical accuracy, and contribute practical engineering knowledge that enhances AI reasoning and performance. No prior AI experience is required—your engineering expertise is what matters most. Key Responsibilities - Technical Review & Analysis - Review and analyze engineering documentation, including technical reports, design documents, specifications, and project analyses. - Evaluate engineering submissions for technical accuracy, completeness, clarity, and adherence to industry best practices. - Provide detailed technical feedback, clarifications, and expert recommendations on complex engineering scenarios. - Content Development - Develop real-world engineering examples, case studies, and problem sets across various engineering disciplines. - Create structured technical explanations to help improve AI understanding of practical engineering concepts. - Ensure all technical content is accurate, well-organized, and aligned with engineering principles. - AI Model Evaluation - Assess AI-generated engineering responses for correctness, logical reasoning, and technical rigor. - Identify errors, inconsistencies, or gaps in engineering outputs and provide actionable feedback to improve model performance. - Contribute to the development of high-quality engineering datasets for AI training and evaluation. - Collaboration & Process Improvement - Work closely with project teams to ensure deliverables align with project milestones and quality standards. - Share industry best practices and technical insights to enhance project methodologies. - Collaborate effectively in a remote environment while maintaining high-quality documentation and deliverables. Qualifications - Bachelor's, Master's, or PhD in Mechanical, Civil, Electrical, Chemical, Industrial, Aerospace, Electronics, Manufacturing, or any other Engineering discipline. - Experience preparing technical reports, engineering documentation, design specifications, or project analyses. - Strong analytical and problem-solving skills with attention to technical detail. - Excellent written and verbal communication skills with the ability to explain complex engineering concepts clearly. - Ability to work independently and manage deliverables in a remote work environment. Requirements - Experience working on multidisciplinary or cross-functional engineering projects. - Ability to communicate technical concepts effectively to both technical and non-technical audiences. - Familiarity with engineering standards, regulatory compliance, quality assurance, or industry best practices. - Experience reviewing engineering documentation or providing technical guidance. - Passion for innovation, technology, and engineering education. Must-Have Skills - Engineering Documentation - Technical Analysis - Technical Report Writing - Problem Solving - Engineering Design Review Good-to-Have Skills - Quality Assurance - Regulatory Compliance - Technical Documentation - Cross-functional Collaboration - Engineering Standards - Project Analysis - Design Documentation - Research & Technical Writing

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