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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.
- Guide organizations through:
- EU AI Act
- NIS2
- ISO/IEC 42001
- Help establish:
- AI inventories
- Approval workflows
- Risk classifications
- Audit evidence
- Practical AI operating models.
Scope & Shape AI Projects
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- 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 technical projects from ambiguous business requirements.
- Excellent communication skills—from board-level conversations through to deep technical discussions.
- Comfortable working autonomously in fast-moving client environments.
- Willingness to travel (~40%).
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Strong Pluses
- AI Governance & Compliance:
- EU AI Act
- NIS2
- ISO/IEC 42001
- NIST AI RMF
- Gartner AI TRiSM
- AI Security:
- Prompt injection
- Data leakage
- Agent permissions
- AI-SPM / DSPM
- Experience with:
- Model Context Protocol (MCP)
- Agent runtimes
- Pinecone
- Milvus
- Weaviate
- Chroma
- Enterprise data governance, backup, resilience, or Microsoft 365 ecosystems.
- Experience delivering into regulated industries:
- Public Sector
- Defence
- Financial Services
- Healthcare
- Experience in air-gapped or sovereign cloud environments.
- Previous Forward Deployed Engineering, embedded consulting, or customer-facing engineering experience.
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