Forward Deployed Engineer - AI

Avepoint · Munichsenior

En corto

▸Ingeniero técnico avanzado que se incorpora directamente en clientes para construir y gobernar soluciones de IA.
▸Haces desde talleres con directivos hasta desarrollo de prototipos en producción con LLMs y gobernanza.
▸Tu principal diferenciador: eres el ingeniero que entrega resultados reales, no solo hablas de ellos.

Fluency in English is required for client-facing engagements.

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

¿Qué piden?

✓5+ años en ingeniería de software, arquitectura de soluciones o consultoría técnica.
✓Al menos 2 años de experiencia práctica con sistemas de IA/LLM en proyectos reales.
✓Capacidad para guiar talleres de gobernanza y seguridad de IA con ejecutivos y equipos técnicos.
✓Experiencia en construir prototipos y componentes productivos con LLMs (Azure OpenAI, AWS Bedrock, etc.).
✓Habilidad para traducir necesidades de negocio en scopes técnicos concretos y entregables.
✓Experiencia en entornos regulados o aislados (air-gapped) con adaptadores personalizados.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

Azure OpenAIAWS BedrockGoogle VertexAnthropicRAG pipelinesLLM integrationsMCP-based tool integrationsAI inventoriesrisk classificationapproval workflows

¿A quién escribirle en Avepoint?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

Enterprises are adopting AI faster than they can govern it — and they are looking for a partner who can do two things at once: speak credibly about AI trust, governance, and security, and actually build. The Forward Deployed Engineer (AI) is that partner. You are the technical face of AvePoint inside client organizations: equally comfortable whiteboarding AI trust and governance concepts with a CISO, translating a business problem into a scoped AI build project, and writing the first working prototype yourself. You embed with clients, ship real outcomes, and own the engagement end to end. This is not a pre-sales role with a demo script, and not a back-office delivery role. It is the engagement model pioneered by leading AI companies for their strategic enterprise customers: a senior engineer deployed forward, with the autonomy to own the problem from first workshop to production. What you'll do Advise on AI trust and governance. Lead workshops that help clients understand and take control of their AI landscape — agents, copilots, models, and the data behind them, including the shadow AI they didn't know about. Explain AI governance, security posture, and resilience concepts credibly to both technical teams and executives. Guide clients through obligations such as the EU AI Act, NIS2, and ISO 42001, and help them stand up practical operating models: AI inventories, approval workflows, risk classification, and audit evidence. Scope and shape AI build projects. Sit with business stakeholders to understand the underlying need behind "we want AI for X." Identify the highest-value use cases, define success criteria, and translate ambiguous requirements into concrete, estimable technical scopes — architecture outlines, data and integration requirements, delivery phases, effort and risk assessments. Write statements of work that engineering teams can actually deliver and clients can actually sign. Build and deliver. Develop prototypes and production components for client AI solutions: agent workflows, RAG pipelines, LLM integrations (Azure OpenAI, AWS Bedrock, Google Vertex, Anthropic), MCP-based tool integrations, and the governance and security controls around them. Deliver custom adapters and local tooling for regulated, cloud-restricted, or air-gapped environments where standard SaaS approaches cannot go. Own the relationship through delivery. Act as the trusted technical advisor from first workshop through go-live: run enablement sessions, support adoption, troubleshoot in production, and expand the engagement where you see genuine value for the client. What we're looking for Must-haves • 5+ years in software engineering, solutions architecture, or technical consulting, with at least 2 years hands-on with modern AI/LLM systems in real projects (not only experimentation). • Practical experi

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