Note: The job is a remote job and is open to candidates in USA. H.W. Kaufman Group is building an AI Value Engineering capability inside Information Technology to engineer measurable business value across the enterprise. This role is for a hands-on, AI-enabled developer who can move across applications, automation, data, and business operations to identify high-impact opportunities, build working solutions, prove their work, and transition responsibly to sustaining teams.
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 is uncertain, 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 while validating architectural 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 business workflows so solutions are grounded in how work gets done
Skills
- Professional software development experience in enterprise environments with 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 to adopt them quickly 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 to enterprise 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 to operate inside appropriate enterprise guardrails while still challenging assumptions and finding better approaches
- Information security — secure development practices, identity and access management, data protection, secrets management, vulnerability remediation, secure API design, or practical experience partnering with security teams
- Azure and cloud services — 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 / DevOps — Terraform, Bicep, ARM templates, CI/CD pipelines, environment configuration, deployment automation, observability, or release governance
- Enterprise integration — API gateways, MuleSoft, event-driven integration, message queues, service orchestration, or integration monitoring
- AI governance and operationalization — prompt/version management, LLM evaluation, data leakage prevention, human-in-the-loop review, auditability, monitoring, or safe deployment of AI-assisted workflows
Benefits
- Remote: Yes
Company Overview