AI Engineer (f/m/x)

Mercedes-Benz.io GmbH•Waller, WA

1d•Hybrid

About The Position

As an AI Engineer, you will help shape the next generation of AI-powered products and experiences at Mercedes-Benz.io. Working at the intersection of software engineering and AI, you will design, build, and scale intelligent solutions that solve real business challenges and deliver measurable impact. Collaborating with product teams, software engineers, and domain experts, you will develop and operate production-ready AI systems that transform AI capabilities into reliable and scalable outcomes.

Requirements

Proven experience in software engineering, with the ability to design, build, and operate maintainable, scalable, secure, and production-ready systems.
Hands-on experience building AI-powered applications with LLMs, including prompting strategies, structured outputs, tool integration, or agentic workflows.
Strong understanding of AI system design, agent architectures, orchestration patterns, state management, and tool integration approaches.
The ability to evaluate technical and business trade-offs, selecting the most appropriate solution rather than the most complex one.
Experience designing and implementing retrieval and knowledge systems, including embeddings, retrieval pipelines, grounding strategies, context engineering, and retrieval-augmented generation (RAG) approaches.
Knowledge of AI evaluation methodologies, including test design, quality assessment, regression testing, performance measurement, and failure analysis.
Understanding of AI systems engineering practices, including observability, monitoring, reliability, troubleshooting, cost optimisation, and operational readiness.
Knowledge of Responsible AI principles, including privacy, data protection, bias awareness, accountability, human oversight, and relevant AI regulations.
Strong collaboration and communication skills, with the ability to work effectively with both technical and non-technical stakeholders.
Critical thinking, problem-solving ability, learning agility, and a mindset of continuous improvement, with a willingness to share knowledge and help others grow as AI technologies evolve.

Responsibilities

Analyze and decompose complex problems, determining where AI-driven reasoning adds value and where deterministic, testable software is the better solution.
Evaluate and select models, frameworks, and technologies, balancing quality, latency, scalability, cost, and production readiness.
Design, build, and operate AI systems and agentic solutions, including single-agent and multi-agent architectures, applying sound engineering judgment when deciding where agentic approaches add value.
Develop AI-powered applications using LLMs, prompts, structured outputs, tool-calling capabilities, and orchestration logic, treating these components as production-grade software.
Build retrieval and knowledge systems, including retrieval pipelines, embeddings, chunking strategies, grounding mechanisms, and context management approaches that improve response quality and reliability.
Implement Responsible AI practices, including guardrails, privacy protections, security controls, auditability, human oversight, and regulatory compliance.
Define and execute evaluation strategies through test datasets, quality assessments, performance measurement, regression testing, and failure analysis to ensure reliable AI behaviour over time.
Ensure production reliability and observability through monitoring, tracing, logging, metrics, performance management, and operational readiness practices.
Investigate and resolve AI system failures, identifying root causes across retrieval, prompting, context management, model behaviour, planning, and tool usage while designing recovery and fallback mechanisms that improve system resilience.
Contribute to shared AI platforms and engineering practices by building reusable components, promoting standards, and sharing knowledge across the engineering community.
Act as an advocate for AI engineering excellence, helping grow AI capabilities across teams through knowledge sharing, best practices, and technical enablement.
Collaborate closely with stakeholders and domain experts to define requirements, validate outcomes, communicate limitations, and foster appropriate trust in AI-powered solutions.