AI Engineer (f/m/x)

Posted Yesterday
Be an Early Applicant
2 Locations
Hybrid
Entry level
Biotech • Pharmaceutical
The Role
Design, build, and operate production-ready AI systems and agentic applications using LLMs, retrieval pipelines, orchestration, and tool integration. Evaluate models and system quality, implement responsible AI controls, and ensure reliability through monitoring, testing, observability, and failure analysis. Collaborate with product teams and domain experts to deliver scalable AI solutions, define requirements, communicate limitations, and promote AI engineering standards across teams.
Summary Generated by Built In

HEY THERE!
We are Mercedes-Benz.io. Our mission is to ignite and build digital solutions for Mercedes-Benz by bringing together a tribe of digital enthusiasts who drive the digital future of mobility.


We believe flexibility and collaboration go hand in hand. With offices in Lisbon and Braga, we offer a flexible hybrid setup, with an average of one day per week in the office. This allows for meaningful in-person collaboration while maintaining the flexibility to work in a way that best supports you and your team.

We do not care about your shoes, as long as you bring the right attitude. At Mercedes-Benz.io we walk the talk and make things happen. We do not digitise for the sake of being digital, but to create real value. We reflect, challenge the status quo, and stand up for each other. This is why we hire people who want to make an impact.

ABOUT THE ROLE

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.

IN THIS ROLE YOU WILL

  • 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.

TO SUCCEED YOU NEED

  • 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.

YOU WILL BE HAPPY WITH

  • Flexible hybrid work model

  • Open source software

  • No top‑down hierarchy. We trust in your self‑organization

  • Colleagues who are smart, driven, and collaborative

  • An open‑minded and informal culture backed by a global brand

  • Health insurance and life insurance

  • Proactive self‑development through international trainings and conferences

  • Brand Connection perks

  • Much more cool stuff

THIS IS WHAT OUR PROCESS LOOKS LIKE

  • Screening Call with our recruiter

  • Take home challenge

  • Technical Interview

  • Culture Interview

  • Offer 

WHAT YOU'LL NEED TO KNOW ABOUT US

Mercedes-Benz.io develops software and technology for the digital platforms of Mercedes-Benz. Our main products are the Mercedes-Benz Website worldwide, the e-commerce platform, digital services and aftersales solutions.

We have a fantastic team with people from all over the world. We encourage frequent collaboration and pairing across disciplines. English is the main working language.

Mercedes-Benz.io is an equal opportunities employer. We believe that diverse experiences and a broad collective perspective lead to a better company culture and better products.

Please send your resumé in English and PDF.

Skills Required

  • Proven experience in software engineering, including designing, building, and operating maintainable, scalable, secure, production-ready systems
  • Hands-on experience building AI-powered applications with LLMs, including prompting, structured outputs, tool integration, or agentic workflows
  • Strong understanding of AI system design, agent architectures, orchestration patterns, state management, and tool integration
  • Ability to evaluate technical and business trade-offs and select appropriate solutions
  • Experience designing and implementing retrieval and knowledge systems, including embeddings, retrieval pipelines, grounding, context engineering, and RAG
  • 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 optimization, 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 technical and non-technical stakeholders
  • Critical thinking, problem-solving ability, learning agility, and continuous-improvement mindset
Am I A Good Fit?
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The Company
3,500 Employees
Year Founded: 2012

What We Do

Mustang Bio, Inc. is a clinical-stage biopharmaceutical company focused on translating medical breakthroughs in cell and gene therapies into potential cures for difficult-to-treat cancers and autoimmune diseases.

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