Senior AI Engineer

Project: Multi‑Agent AI System in the Automotive Sector
 Location: 100% Remote (On-site onboarding in Munich possible)
 Workload: Full-time (100%)


Project Context
A strategic AI initiative in the automotive sector aims to transform an existing single-agent prototype into a robust, production-ready multi-agent vehicle configurator within a Microsoft/Azure-based enterprise environment.
 Focus is on designing a scalable and secure multi-agent architecture with defined orchestration, deterministic behavior, high performance (<2–4 sec latency), and enterprise-grade integration.

Project Objectives

• Build a multi-agent architecture (orchestrator + specialized agents)
• Prepare a production-ready Azure implementation
• Achieve performance targets (<2–4 seconds latency)
• Implement guardrails & prompt-injection protection
• Ensure clean API integrations and orchestration
• Establish enterprise-level observability, security & monitoring

Responsibilities
The Senior AI Engineer will take a leading architectural and hands-on implementation role:

1. Refactor the existing single-agent prototype into a multi-agent system
2. Design and document a structured orchestration graph
3. Implement strict tool permissioning and scope control
4. Manage deterministic loops, retries, and fallback logic
5. Build a robust session & state model
6. Integrate external APIs with resilient error handling
7. Implement telemetry & monitoring hooks across agents
8. Optimize latency and reduce unnecessary tool calls
9. Implement guardrails (prompt-injection prevention, output validation)
10. Communicate architectural decisions clearly to stakeholders

Technical Requirements (Must-Have)
Core AI / LLM Engineering

• Function Calling, JSON Schema outputs
• Multi-agent system design (orchestrator + experts)
• Retry logic, fallback strategies, healing loops
• Guardrails: prompt-injection protection, scope control, output validation

Backend Engineering

• Python (ideally FastAPI), Async IO, Pydantic
• Production-grade API design (timeouts, retries, logging, error handling)
• Versioned schema & interface contracts
• Multi-API integrations with robust error-handling patterns

Azure Production Setup

• Azure Foundry
• Managed Identity
• Key Vault
• Log Analytics / Application Insights
• Understanding of private endpoints, API Gateway & enterprise security
• Solid grasp of enterprise deployment & monitoring practices

Architecture & Systems Thinking

• Orchestration graph design & documentation
• Session/state model concepts
• Latency budgets, performance awareness
• Ability to determine when deterministic logic > LLM-based logic

Nice-to-Have

• MCP experience
• Constraint systems
• Recommendation systems

Candidate Requirements

• Senior-level experience in AI engineering
• Strong system and architecture thinking
• Ability to document complex relationships clearly
• Previous experience in near-production enterprise environments
• Clear, precise communication

Soft Skills

• Structured, analytical working style
• High personal responsibility
• Architecture-driven, solution-oriented mindset
• Strong communication with technical & non-technical stakeholders