29 days 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
