Forward Deployed Engineer - AI
AvePoint · London, England, United Kingdom; Munich, Germany
New
mid
forward deployed
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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 organisations 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
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%).
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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Posted 2026-07-15