3 months ago
Munich, Germany or London, United KingdomSenior
Responsibilities
- Lead enterprise workshops on AI trust, governance, security posture, resilience, inventories, approval workflows, risk classification, and audit evidence.
- Advise technical teams and executives on AI governance obligations, including the EU AI Act, NIS2, and ISO 42001.
- Identify valuable AI use cases and translate ambiguous business requirements into technical scopes, architecture outlines, data and integration requirements, delivery phases, estimates, and risks.
- Write practical statements of work for client AI engagements.
- Build prototypes and production components including agent workflows, RAG pipelines, LLM integrations, MCP-based tools, governance controls, and security controls.
- Deliver custom adapters and local tooling for regulated, cloud-restricted, sovereign, or air-gapped environments.
- Own client engagements from initial workshop through go-live, including enablement, adoption support, production troubleshooting, and identifying appropriate expansion opportunities.
- Travel to client sites and operate autonomously in ambiguous, fast-moving engagements.
Requirements
- At least 5 years of experience in software engineering, solutions architecture, or technical consulting, including at least 2 years of hands-on experience with modern AI/LLM systems in real projects.
- Practical experience building with LLM APIs and frameworks such as Azure OpenAI, Bedrock, Vertex, LangChain, or Semantic Kernel, including RAG, agentic workflows, and tool or function calling.
- Hands-on machine learning experience covering model development, evaluation, deployment, and operationalization, with enterprise AI, predictive analytics, and scalable MLOps experience.
- Strong programming skills in Python and/or C# or TypeScript.
- Working fluency with Azure, AWS, or GCP, including identity, networking, and data services.
- Experience scoping technical projects from ambiguous business requirements and producing credible delivery plans with phases, estimates, and risks.
- Excellent communication skills with senior stakeholders and the ability to explain AI governance and technical trade-offs to both executives and engineers.
- Willingness to travel to client sites and work with high autonomy.
- Knowledge of AI governance and compliance frameworks such as the EU AI Act, NIS2, ISO/IEC 42001, NIST AI RMF, or Gartner's AI TRiSM model is a strong plus.
- Experience with AI security topics, including prompt injection, data leakage, agent permissioning, and model or data security posture concepts, is a strong plus.
- Familiarity with MCP, agent runtimes, or vector databases such as Pinecone, Milvus, Weaviate, or Chroma is a strong plus.
- Background in enterprise data governance, security, backup or resilience, Microsoft 365, or multi-cloud environments is a strong plus.
- Experience delivering into regulated industries or air-gapped and sovereign environments is a strong plus.
- Prior forward-deployed, embedded consulting, or customer-facing engineering experience is a strong plus.
- Additional languages relevant to the regional client base are a strong plus.
Benefits
- The role includes travel to client sites and embedded, customer-facing engagement work.
- Within the first 6–12 months, success is measured by leading workshops for multiple enterprise clients, scoping and winning a significant engagement, and delivering working software into a client environment.
Tech Stack
Categories
Forward Deployed
About AvePoint
AvePoint builds SaaS backup, migration, and governance software for enterprises running Microsoft 365, SharePoint, Google Workspace, and Salesforce. Its products help secure data, manage compliance, and automate administration, sold via subscriptions and channel partners. Founded in 2001 and headquartered in Jersey City, the company is publicly traded on the NASDAQ.