Artificial Intelligence Consultant

We are supporting a growing AI product company that is developing an enterprise AI platform for serious production environments.

They are looking for a hands-on AI Architect who can shape the technical direction of the AI platform while remaining close enough to the engineering to validate architectural decisions through code.

This is not a purely advisory or presentation-led architecture position. You will make practical technical decisions based on previous experience of designing, deploying and operating AI systems in production.

Your responsibilities

• Define the architecture for production AI agents and applications

• Shape multi-tenant RAG architecture, retrieval strategy and data isolation

• Design vector search, hybrid retrieval, re-ranking and index lifecycle processes

• Establish evaluation, observability and release standards

• Design for explainability, audit lineage, decision provenance and security

• Define model access, routing, tool integration and tenant-level usage controls

• Make architecture decisions covering quality, latency, token usage, cost and reliability

• Align the AI layer with APIs, service connectivity and Kubernetes infrastructure

• Define Model Context Protocol integrations with enterprise tools and services

• Produce architecture decision records, technical standards and operational runbooks

• Build or review reference implementations for critical parts of the platform

• Support engineers with technical direction, design reviews and production problems

Must-have technical experience

Applicants must be able to demonstrate hands-on production and architectural experience across the following technology environment:

• Python

• FastAPI

• PydanticAI

• LangGraph

• LiteLLM

• Langfuse

• PostgreSQL and pgvector

• Model Context Protocol

• Kubernetes

• Production AI agents and agentic applications

• Multi-tenant RAG architecture

• Vector search, hybrid retrieval and re-ranking

• Index design and lifecycle management

• LLM and retrieval evaluation frameworks

• LLM observability and production monitoring

• API and enterprise service integration

• CI/CD, automated testing and controlled production releases

• Tenant isolation, access controls and usage quotas

• Monitoring latency, token consumption, cost, failures and retrieval quality

• Explainability, audit trails, decision provenance and operational traceability

• Secure AI systems within regulated or highly governed environments

What we are looking for

• Substantial professional Python and AI engineering experience

• A proven record of designing, deploying and operating AI or LLM systems in production

• Experience making architectural decisions for enterprise AI platforms

• Strong understanding of production reliability, failure modes and system performance

• Enough hands-on ability to validate architectural decisions through code

• Experience documenting technical decisions, trade-offs and operational procedures

• The ability to explain how systems performed under load and where they failed

• Experience improving architecture based on incidents, evaluation results and user behaviour

• Residence and work authorisation in Germany

• Willingness to attend occasional team meetings in Munich

Experience within banking, insurance, pharmaceuticals, healthcare or another regulated environment would be particularly relevant.

What you can expect

• Salary of up to €130,000, depending on experience

• Virtual Stock Option Plan participation

• Fully remote working within Germany

• 30 days’ annual leave

• A choice of Edenred meal and shopping vouchers or EGYM Wellpass

• Significant influence over the platform’s technical direction

• Architectural responsibility from your first project

• Direct access to the leadership and AI teams

• Professional exchange with highly experienced AI specialists

• A modern technology environment with room to test and evaluate new approaches

• Occasional team meetings in Munich

When applying, please include a short and specific answer to this question:

What AI system have you personally implemented into production, how long has it been running and what is its biggest weakness today?