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The Role

Fusion Risk Management is seeking a cloud-native, AI-first full stack engineer to design and deliver a new generation of AI-enabled professional services tools. These tools will empower our consulting and services teams to work more efficiently, consistently, and at scale — automating the most repetitive, complex, and error-prone parts of their workflows while leaving critical business decisions in human hands.

An early priority will be developing frameworks that analyze and transform Salesforce metadata, with a central challenge being how to organize large metadata sets so AI tools can reliably consume and analyze them. Success will require a clear understanding of today’s AI constraints while also anticipating near-term advances that may reduce those barriers. You’ll design retrieval, chunking, and orchestration pipelines, exploring novel and effective methods for structuring large-scale analyses.

This is a hands-on engineering role for someone who is both practical and visionary: able to ship production-ready applications on Azure while also experimenting with bleeding-edge approaches. If you’re hungry to learn, eager to adopt the latest technologies, and passionate about AI’s potential to transform enterprise software delivery, we’d like you to join us.

Responsibilities

• Design and build cloud-native applications on Microsoft Azure that blend deterministic automation with AI-driven intelligence.
• Deliver a portfolio of AI-enabled tools that streamline professional services delivery, starting with projects focused on Salesforce org analysis and migration support.
• Extend and enhance existing approaches to engaging with Salesforce metadata already in use at Fusion.
• Develop solutions that transform large enterprise metadata sets into structured outputs that AI models can reason about effectively.
• Tackle AI efficiency challenges, including:
• Structuring metadata for effective AI analysis.
• Overcoming context window limits with chunking, retrieval, and orchestration strategies.
• Exploring approaches (including Graph RAG–style concepts) for organizing large-scale data analyses.

• Leverage Cursor, ChatGPT Pro, and Azure OpenAI as both development accelerators and embedded components in delivered applications.
• Build human-in-the-loop workflows, enabling consultants to review, approve, and validate AI outputs.
• Establish robust engineering foundations: logging, manifests, rollback strategies, and audit-friendly outputs.
• Collaborate with Fusion architects and consultants to ensure tools are usable, scalable, and aligned with real-world service delivery needs.
• Continuously evaluate emerging technologies and identify opportunities to expand Fusion’s AI-augmented professional services automation roadmap.

Knowledge, Skills, and Abilities

• Strong cloud-native engineering experience on Microsoft Azure (required).
• Proficiency in at least one modern language (Java, Python, C#, TypeScript, Go). Flexibility in stack is encouraged as long as solutions run effectively on Azure.
• Deep understanding of AI development and integration, including:
• Context window limitations and data-reduction strategies.
• Prompt engineering and multi-step workflow design.
• Retrieval-augmented generation pipelines.
• Human-in-the-loop design for safe, reliable AI systems.
• Experience building schema analysis, comparison, and orchestration frameworks.
• Familiarity with Salesforce metadata and APIs (objects, fields, relationships) sufficient to parse and process org structures.
• Proficiency with Bitbucket for source control and CI/CD pipelines.
• Creative, curious, and ambitious: excited to learn, experiment, and apply bleeding-edge approaches.