Do you want to take ownership of AI solutions from first idea to production rollout? Are you keen to turn complex business requirements into robust and scalable AI‑enabled systems? Do you want to use your technical expertise to create real impact for national and international clients?
As part of our AI development team, you work directly on challenging consulting projects for national and international clients
You take end‑to‑end ownership of solution design for AI‑enabled products – from discovery and PoC through to production rollout
You translate business requirements into clear technical concepts, including architecture, interfaces, data flows, and quality and acceptance criteria
When designing and validating systems, you consider scalability, security, latency, reliability and cost.
You build and iterate prototypes, for example using prompting, RAG approaches, agents or function calling, to validate feasibility and business value early on
You actively help define standards and patterns for AI and LLM solutions – from model selection and prompt and context design to evaluation strategies and quality gates
You establish LLMOps and MLOps practices, including versioning, CI/CD, automated testing, monitoring, feedback loops and continuous improvement
You integrate AI capabilities into existing platforms, for example via APIs and event‑driven or service‑oriented architectures
You embed responsible AI by design, taking data privacy (GDPR), security, bias and safety considerations, and auditability into account from the start
As a technical sparring partner, you support and mentor engineers, align with stakeholders and communicate complex topics clearly to non‑technical audiences
A degree in Computer Science, Information Technology or equivalent professional experience
A strong software engineering background (backend and/or full stack), combined with a product mindset and consulting experience
Hands‑on experience delivering (generative) AI solutions into production, not limited to demos or prototypes
Solid knowledge of common LLM application patterns, including RAG, graph‑based approaches, embeddings, vector search, tool or function calling, agents and guardrails
Experience with at least one LLM framework or stack, such as Semantic Kernel, LangChain or LlamaIndex, as well as structured prompt engineering
The ability to evaluate AI systems systematically, including offline and online evaluation, benchmark design, qualitative reviews and regression testing for prompts and retrieval
Experience with distributed systems and modern integration patterns such as APIs, microservices, service‑oriented architectures and event‑driven design
Proficiency in at least one programming language and framework, for example Python, TypeScript/Node.js, C# or Java, along with strong API design skills
Solid cloud knowledge across Azure, AWS or GCP; experience with Azure AI services (e.g. Azure OpenAI, Azure AI Search, Document Intelligence) is a plus
A DevOps mindset, including Git, CI/CD, containers, infrastructure as code (e.g. Terraform or Bicep), observability (logs, metrics, tracing) and cost awareness
Security fundamentals, such as OAuth2/OIDC, authentication and authorisation, secrets management, encryption and secure coding practices
Technical leadership or mentoring experience is a plus, complemented by strong communication skills, a proactive attitude, problem‑solving capability and attention to detail
Fluent English for close collaboration within the team; German is a plus, but not mandatory
A varied role with a high level of responsibility
Continuous learning and development opportunities
A mentoring programme
A friendly, close‑knit and supportive working environment
Team events and company retreats
Mobility benefits
Great, centrally located office in Munich
Free drinks, fruit and yoghurts in the office
Company laptop and company mobile phone
Sports and fitness offers
#LI-DNI
