Advisor, AI Solutions Engineer

Mercedes-Benz USA • Atlanta, GA

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About The Position

The Adv, AI Solutions Engineer is a hands-on technical role within Mercedes-Benz USA (MBUSA) Information Technology. You will partner with business and technology teams to identify high-value opportunities and transform AI concepts into secure, scalable, production-ready solutions. Working across AI engineering, enterprise architecture, data, integration, DevSecOps, cybersecurity, and product teams, you will deliver measurable improvements in productivity, quality, operational resilience, and customer and dealer experiences while supporting Mercedes-Benz standards.

Join MBUSA and help shape how AI is applied responsibly and at scale within a globally recognized automotive company. You will work directly with teams across the organization, turn high-value ideas into dependable solutions, and establish engineering practices that enable faster delivery, stronger controls, and sustained business impact.

Frontier Impact: Work at the cutting edge of applied AI inside a large enterprise. You will have access to the latest AI platforms and cloud services, with the mandate to create and deploy them at scale where they create the most operational value.

Direct, Measurable Outcomes: Your work will directly improve internal productivity, automation, and innovation. You will see the impact of your deployments in real-time — in the tools your colleagues use every day.

Shape AI Culture: Opportunity to define AI adoption culture from the inside. As an early mover in this role, you will establish the patterns, practices, and standards that govern how the entire organization thinks about and uses AI — a lasting legacy beyond any single project.

MBUSA is advancing the responsible use of AI across business and IT. This role bridges the gap between experimentation and sustained business value by embedding engineering expertise with the teams that design, build, integrate, secure, and operate enterprise solutions. You will help prove what is possible while establishing reusable patterns, production controls, and measurable outcomes that can scale across the organization.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field, or an equivalent combination of education and experience.
• Nine or more years of software, platform, cloud, integration, or solution engineering experience, including recent hands-on delivery of AI-enabled solutions.
• Practical experience with large language models, generative AI, RAG, prompt and evaluation techniques, and cloud AI services on Azure, AWS, or Google Cloud.
• Strong programming skills in Java, Python, and/or TypeScript/JavaScript.
• Experience with APIs, microservices, identity and access controls, CI/CD, containers, observability, and enterprise integration patterns.
• Strong analytical, problem-solving, communication, and stakeholder-management skills.

Nice To Haves

• Experience in a forward-deployed, embedded, consulting, enablement, or customer-facing engineering role.
• Experience designing and operating agentic AI or RAG solutions in regulated or security-conscious enterprise environments.
• Knowledge of responsible AI, AI governance, cybersecurity, data protection, model evaluation, third-party risk, and human-in-the-loop controls.
• Experience with enterprise application, cloud, integration, DevSecOps, ITSM, architecture, or data platforms in a large, complex organization.
• Relevant cloud, AI, architecture, security, or DevOps certification.
• Builder's Mindset - Enjoys turning ambitious ideas into production-ready solutions, not just prototypes.
• Cross-Functional Collaboration - Builds trust quickly, listens actively, and partners effectively across engineering, data, security, and business teams.
• Bias Toward Action - Prefers practical, pragmatic solutions over perfect ones; delivers, learns, and iterates.
• Passion for Broad AI Enablement - Is energized by making AI accessible and impactful across the organization, not just for technical specialists.
• Intellectual Curiosity - Stays current with the rapidly evolving AI landscape and brings relevant innovations back to the team with clear business context and applicability.

Responsibilities

• Own the technical lifecycle of priority AI use cases—from discovery and feasibility assessment through architecture, development, production deployment, adoption, monitoring, and continuous improvement.
• Partner with business and IT teams to identify, assess, and prioritize AI use cases based on business value, feasibility, risk, data readiness, and time to value.
• Translate business needs into secure and scalable solution designs, prototypes, and production implementations with defined owners, success metrics, and measurable outcomes.
• Build AI-enabled applications, agents, retrieval-augmented generation solutions, integrations, APIs, and automated workflows across approved Azure, AWS, SaaS, and hybrid environments.
• Develop reusable architecture patterns, reference implementations, evaluation methods, and CI/CD controls that accelerate delivery across teams.
• Evaluate emerging AI models, platforms, and agent frameworks for enterprise applicability, security, supportability, cost, and production readiness.
• Provide hands-on technical guidance and enablement to internal engineering teams on AI tools, frameworks, and best practices.
• Conduct architecture reviews, code walkthroughs, and technical workshops to upskill internal teams on AI integration patterns.
• Act as a trusted advisor and go-to resource for AI-related questions across engineering disciplines.
• Collaborate with Enterprise Architecture, Cybersecurity, Data Protection, Legal and Compliance, data, cloud platform, integration, and DevSecOps teams to implement approved controls and architecture standards.
• Work with business owners, product teams, and program leaders to align AI initiatives with MBUSA priorities, roadmaps, funding decisions, and value-realization targets.
• Coordinate effectively with Mercedes-Benz global teams, regional stakeholders, vendors, and service providers to reuse enterprise capabilities and avoid duplicative solutions.
• Communicate complex AI concepts, risks, dependencies, and trade-offs clearly to both technical and non-technical stakeholders.
• Maintain solution architectures, model and data-flow documentation, runbooks, integration guides, decision records, and support procedures for deployed AI solutions.
• Implement observability for usage, quality, latency, cost, model behavior, security events, and operational health; respond to incidents and performance degradation through established MBUSA processes.
• Apply privacy by design, least-privilege access, human oversight, secure software development, and risk-proportionate controls throughout the AI lifecycle.
• Use performance, adoption, user feedback, and value metrics to iterate, scale, pause, or retire solutions as appropriate.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior