Responsibilities H.W. Kaufman Group is building an AI Value Engineering capability inside Information Technology to engineer measurable business value across the enterprise. The AI Value Engineering teamoperatesat the intersection of application development, RPA, data, business operations, and AI-enabled delivery. Its charter is to deliver high-impact solutions across quick-strike and deep-dive engagements, partner closely with business stakeholders, transition proven solutions to sustaining teams, and stop initiatives quickly when they are not proving value.The team is measured on portfolio impact, successful handoffs, quantified business value, disciplined focus, and the ability to avoid becoming a maintenance or production-support function. This role is for a hands-on, AI-enabled developer who can move across applications, automation, data, and business operations toidentifyhigh-impact opportunities, build working solutions, prove their work, and transition responsibly to sustaining teams. We are looking for developers for whom building software is more than a job. Strong candidates are resourceful, curious, disciplined, and energized by solving problems. They have opinions shaped by experience, reading, experimentation, and exposure to good engineering practices. They understand that AI changes the speed of software delivery, but not the need for judgment, craftsmanship, business understanding, and accountability. This role is best suited for a T-shaped or comb-shaped technologist: broad enough to wire together platforms, data, APIs, automations, and user workflows; deep enough to make sound technical decisions and avoid fragile solutions. What Success Looks Like Take a loosely defined business problem, clarify the value hypothesis, and produce a working demonstration quickly Know when an AI agent92s recommendation is plausible, risky, over-engineered, or misaligned with enterprise supportability Move comfortably between front-end, back-end, integration, data, workflow automation, and platform configuration Actively look for simpler, faster, more supportable paths rather than merely implementing the first technical answer Produce solutions that can be handed off cleanly; you do not create hidden dependencies on yourself Communicate tradeoffs clearly to technical and non-technical audiences Responsibilities Deliver quick-strike solutions in days or weeks where business value is clear and the path is known Contribute to deeper exploratory engagements over one to three months where the solution isuncertain,but the potential value is high Build prototypes, automations, integrations, data-driven workflows, and application features across Salesforce, MuleSoft, .NET, RPA, AI/ML tooling, and related enterprise platforms Use AI development tools such as Claude, Codex, Gemini, Copilot, or similar agents to accelerate delivery whilevalidatingarchitectural fit, maintainability, security, and operational risk Work directly with underwriting, claims, operations, and IT stakeholders to understand workflows, pain points, value drivers, and practical adoption constraints Translate business problems into working software, measurable experiments, and clear handoff packages Document architecture, configuration, dependencies, known limitations, prompts, runbooks, and operational considerations for the receiving team Recommend pivoting or stopping work when an initiative is not proving valuable, without treating that as failure Shadow and learn businessworkflowsso solutions are grounded in how work gets done Qualifications Professional software development experience in enterprise environmentswith 5+ years of experience Full-stack capability across application development, integrations, APIs, data access, workflow automation, and user-facing delivery Experience with at least several of the following: Salesforce, MuleSoft, .NET/C#, JavaScript/TypeScript, SQL, REST APIs, RPA/workflow tools, cloud services, CI/CD, or enterprise data platforms Practical experience using AI-assisted development tools or a demonstrated ability toadopt themquickly and responsibly Strong debugging, problem decomposition, and self-directed learning skills Ability to evaluate code quality, maintainability, patterns, security implications, and operational fit Ability to write clear technical documentation and conduct knowledge transfer with receiving teams Comfort working in timeboxed engagements with defined outcomes, checkpoints, and handoff expectations Experience in insurance, underwriting, brokerage, claims, or other workflow-heavy business domains is a plus Exposure toenterprise architecture patterns, design patterns, domain modeling, relational database design, integration patterns, or similar engineering disciplines Experience building automations or AI-enabled solutions that combine applications, data, documents, and human review workflows Experience with prompt engineering, LLM evaluation, retrieval-augmented generation, agentic development workflows, or AI governance practices Ability tooperateinsideappropriate enterpriseguardrails while still challenging assumptions and finding better approaches Complementary Strengths Because this team is intentionally built from generalists who can go deep, we value candidates who bring complementary strengths that broaden the team92s overall capability. Experience in any of the following areas is a plus, but notrequired: Information security92 secure development practices, identity and access management, data protection,secretsmanagement, vulnerability remediation, secure API design, or practical experience partnering with security teams Azure and cloud services92 Azure App Services, Azure Functions, Logic Apps, Azure SQL, Storage Accounts, Key Vault, Service Bus, Entra ID, Azure DevOps, or similar cloud-native services Infrastructure as Code / DevOps92 Terraform, Bicep, ARM templates, CI/CD pipelines, environment configuration, deployment automation, observability, or release governance Enterprise integration92 API gateways, MuleSoft, event-driven integration, message queues, service orchestration, or integration monitoring AI governance and operationalization92 prompt/version management, LLM evaluation, data leakage prevention, human-in-the-loop review, auditability, monitoring, or safe deployment of AI-assisted workflows #LI-CC1