Job Opportunities API

  About AvePoint AvePoint is the global leader in data protection, unifying data security, governance, and resilience to provide a trusted foundation for AI. More than 28,000 customers rely on the AvePoint Confidence Platform to secure, govern, and rapidly recover data across Microsoft, Google, Salesforce, and other cloud environments. With a single platform for lifecycle control, multicloud governance, and rapid recovery paired with clear ownership across the business, we prevent overexposure and sprawl, modernize legacy and fragmented data, and minimize data loss and interruption. Our global partner ecosystem includes approximately 6,000 MSPs, VARs, and SIs, and our solutions are available in over 100 cloud marketplaces. To learn more, visit  www.avepoint.com .   About the Role Enterprises are adopting AI faster than they can govern it, and they're looking for a partner who can do two things exceptionally well: Speak credibly about AI trust, governance and security. Build real AI solutions that solve business problems. As a Forward Deployed Engineer (AI), you'll be the technical face of AvePoint inside enterprise customers. You'll be equally comfortable: Whiteboarding AI trust and governance concepts with CISOs and executives. Translating business challenges into scoped AI delivery projects. Building the first working prototype yourself. You'll embed with customers, own engagements end-to-end, and deliver tangible outcomes. This isn't a traditional pre-sales role or a back-office delivery position. It's a highly autonomous customer-facing engineering role inspired by the engagement models used by leading AI companies—owning problems from discovery workshops through to production. What You'll Do Advise on AI Trust & Governance Lead AI governance and discovery workshops. Help customers understand and govern their AI landscape (agents, copilots, models and shadow AI). Explain AI governance, security posture and resilience to both technical and executive audiences. Help establish: AI inventories Approval workflows Risk classifications Audit evidence Practical AI operating models. Scope & Shape AI Projects Work directly with business stakeholders to understand the real business problem behind AI initiatives. You'll: Identify high-value AI use cases. Define success criteria. Translate ambiguous requirements into deliverable technical scopes. Produce: Architecture outlines Data & integration requirements Delivery phases Effort estimates Risk assessments Write Statements of Work (SoWs) customers can sign and engineering teams can deliver. Build & Deliver Develop both prototypes and production-ready AI solutions including: AI agents RAG pipelines LLM integrations: Azure OpenAI AWS Bedrock Google Vertex AI Anthropic MCP-based tool integrations Governance and security controls You'll also build custom tooling for regulated, cloud-restricted or air-gapped environments where SaaS solutions aren't suitable. Own Customer Delivery Remain the trusted technical advisor throughout the engagement by: Running enablement sessions. Supporting customer adoption. Troubleshooting production issues. Identifying opportunities to expand engagements where genuine customer value exists. What We're Looking For Must-Haves 5+ years in Software Engineering, Solutions Architecture or Technical Consulting. 2+ years building modern AI/LLM solutions in production (not just experimentation). Hands-on experience with: Azure OpenAI AWS Bedrock Google Vertex AI LangChain Semantic Kernel Experience building: RAG solutions Agentic workflows Tool/function calling Strong programming skills in: Python C# TypeScript Experience with Azure, AWS or GCP, including identity, networking and data services. Proven ability to scope techn