AI Productization Engineer

%3Cp style=%22font-family:Arial;%22%3E%3Cspan style=%22background-color:white;color:black;font-family:Calibri,sans-serif;font-size:10.0pt;%22%3E%3Cspan style=%22line-height:normal;%22%3E%3Cstrong%3EAbout Us%3C/strong%3E%3C/span%3E%3C/span%3E%3C/p%3E%3Cp style=%22font-family:Arial;%22%3E%3Cspan style=%22background-color:white;color:black;font-family:Calibri,sans-serif;font-size:10.0pt;%22%3E%3Cspan style=%22line-height:normal;%22%3EMercedes-Benz USA is responsible for the sales, marketing and service of all Mercedes-Benz and Maybach products in the United States.%26nbsp; In our people, you will find tremendous commitment to our corporate values: %27PRIDE = Passion, Respect, Integrity, Discipline, and Execution%27.%26nbsp; Our products and employees reflect this dedication.%26nbsp; We are looking for diverse top-notch individuals to join the Mercedes-Benz Team and uphold these hallmarks.%3C/span%3E%3C/span%3E%3C/p%3E%3Cp style=%22font-family:Arial;%22%3E%26nbsp;%3C/p%3E%3Cp style=%22font-family:Arial;%22%3E%3Cspan style=%22background-color:white;font-family:Calibri,sans-serif;font-size:10.0pt;%22%3E%3Cspan style=%22border:1.0pt none windowtext;line-height:15.0pt;padding:0in;vertical-align:baseline;%22%3E%3Cstrong%3EJob Overview%3C/strong%3E%3C/span%3E%3C/span%3E%3C/p%3E%3Cp%3EThe AI Productization Engineer turns successful AI and machine learning solutions into scalable, supportable, production-ready products. This role defines the standards, integration patterns, deployment methods, and readiness processes needed to move AI capabilities from pilot to enterprise production.%3C/p%3E%3Cp%3EThe ideal candidate brings expertise in software engineering, AI delivery, enterprise integrations, and production operations. This role serves as the bridge between innovation and long-term sustainable business value.%3Cbr%3E%26nbsp;%3C/p%3E%3Cp class=%22MsoNormal%22%3E%3Cstrong%3EResponsibilities%3C/strong%3E%3C/p%3E%3Cp class=%22MsoNormal%22%3E%3Cstrong%3EAI Productization %26amp; Production Readiness (60%)%3C/strong%3E%3C/p%3E%3Cul%3E%3Cli%3ELead the transition of AI and machine learning solutions from pilot to production.%3C/li%3E%3Cli%3EDevelop reusable deployment, integration, and operational frameworks.%3C/li%3E%3Cli%3EEstablish production readiness standards and supportability requirements.%3C/li%3E%3Cli%3EDefine integration patterns connecting AI capabilities with enterprise business systems.%3C/li%3E%3Cli%3EEstablish model and service versioning strategies, rollback procedures, and environment promotion workflows (dev → staging → production) with automated validation gates at each stage.%3C/li%3E%3Cli%3EEnsure solutions meet expectations for reliability, scalability, monitoring, and support.%3C/li%3E%3Cli%3EDrive consistency and repeatability across AI delivery efforts.%3C/li%3E%3Cli%3EImplement data validation and input contracts for AI pipelines to detect and handle upstream data changes before impacting model outputs.%3Cbr%3E%26nbsp;%3C/li%3E%3C/ul%3E%3Cp class=%22MsoNormal%22%3E%3Cstrong%3EArchitecture %26amp; Integration Leadership (20%)%3C/strong%3E%3C/p%3E%3Cul%3E%3Cli%3EDefine reference architectures and integration standards for AI products.%3C/li%3E%3Cli%3EPartner with engineering teams to accelerate solution deployment and adoption.%3C/li%3E%3Cli%3EEvaluate productization technologies, tooling, and engineering approaches.%3C/li%3E%3Cli%3EContribute to architecture reviews and technical planning.%3C/li%3E%3Cli%3EDefine API contracts for AI products, covering versioning, deprecation, rate limits, quotas, throttling, and SDK guidance.%3C/li%3E%3Cli%3EDefine caching and performance strategies for production AI serving, including result caching, request deduplication, and edge optimizations for low latency.%3Cbr%3E%3Cbr%3E%26nbsp;%3C/li%3E%3C/ul%3E%3Cp class=%22MsoNormal%22%3E%3Cstrong%3EOperational Excellence (10%)%3C/strong%3E%3C/p%3E%3Cul%3E%3Cli%3EDevelop standards for monitoring, incident response, deployment governance, and sustainment.%3C/li%3E%3Cli%3EEstablish operational documentation and engineering best practices.%3C/li%3E%3Cli%3EDrive continuous improvement in production support processes.%3C/li%3E%3Cli%3EDefine AI incident management processes, including classification, escalation, post-incident reviews, and handling of model-specific failures like degradation, hallucinations, and data poisoning.%3C/li%3E%3Cli%3EDevelop AI product DR/BC plans, including failover strategies, RTO/RPO targets, and fallback modes (e.g., rules-based logic).%3C/li%3E%3Cli%3EImplement audit logging and traceability for AI decisions, including inference logging, input/output capture, and end to end data lineage.%3Cbr%3E%3Cbr%3E%26nbsp;%3C/li%3E%3C/ul%3E%3Cp class=%22MsoNormal%22%3E%3Cstrong%3ECollaboration %26amp; Technical Leadership (10%)%3C/strong%3E%3C/p%3E%3Cul%3E%3Cli%3ECollaborate with data science, AI engineering, architecture, and business teams.%3C/li%3E%3Cli%3EProvide technical mentorship and guidance to engineering teams.%3C/li%3E%3Cli%3EPromote engineering excellence and sustainable delivery practices.%3Cbr%3E%26nbsp;%3C/li%3E%3Cli%3EProvide technical mentorship and guidance across the AI Engineering organization.%3C/li%3E%3Cli%3ESupport knowledge sharing, cross-training, and engineering excellence initiatives.%3Cbr%3E%26nbsp;%3C/li%3E%3C/ul%3E%3Cp class=%22MsoNormal%22%3E%26nbsp;%3C/p%3E
%3Cp class=%22MsoNormal%22%3E%3Cstrong%3ETechnical Skills %26amp; Tools%3C/strong%3E%3C/p%3E%3Cp class=%22MsoNormal%22%3E%3Cstrong%3ERequired%3C/strong%3E%3C/p%3E%3Cul%3E%3Cli%3EPython and SQL.%3C/li%3E%3Cli%3EAzure Databricks and enterprise AI platforms.%3C/li%3E%3Cli%3EAzure or AWS cloud platforms.%3C/li%3E%3Cli%3EMLflow and MLOps tooling.%3C/li%3E%3Cli%3EAPI development and enterprise integration patterns.%3C/li%3E%3Cli%3EDocker, Kubernetes, and CI/CD pipelines.%3C/li%3E%3Cli%3EProduction operations, observability, and monitoring.%3C/li%3E%3Cli%3EEnterprise application integration experience.%3C/li%3E%3Cli%3EInfrastructure as code (Terraform or equivalent)%3C/li%3E%3Cli%3ETesting frameworks and strategies for AI systems, including integration testing, performance/load testing (Locust or equivalent),%3Cbr%3E%3Cbr%3E%26nbsp;%3C/li%3E%3C/ul%3E%3Cp class=%22MsoNormal%22%3E%3Cstrong%3EPreferred Skillset%3C/strong%3E%3C/p%3E%3Cul%3E%3Cli%3ESalesforce integration.%3C/li%3E%3Cli%3EServiceNow integration.%3C/li%3E%3Cli%3ESAP integration.%3C/li%3E%3Cli%3EAzure API Management or AWS API Gateway.%3C/li%3E%3Cli%3EApache Airflow or Databricks Workflows.%3C/li%3E%3Cli%3EOpenTelemetry and advanced observability tooling.%3C/li%3E%3Cli%3EExperience supporting AI and agent-based solutions in production.%3C/li%3E%3Cli%3ECaching technologies (Redis, Azure Cache, CDN)%26nbsp;%3C/li%3E%3Cli%3E%3Cp%3EResponsible AI tooling for production, including fairness monitoring, bias detection, and%26nbsp;%3C/p%3E%3Cp%3Eexplainability frameworks (e.g., SHAP, LIME, Azure Responsible AI).%3C/p%3E%3Cp%3E%26nbsp;%3C/p%3E%3C/li%3E%3Cli%3EModern API patterns (GraphQL, gRPC, WebSockets) for high-performance and real-time AI product interfaces.%3Cbr%3E%3Cbr%3E%26nbsp;%3C/li%3E%3C/ul%3E%3Cp class=%22MsoNormal%22%3E%3Cstrong%3EQualifications%3C/strong%3E%3C/p%3E%3Cp class=%22MsoNormal%22%3E%3Cstrong%3ERequired%3C/strong%3E%3C/p%3E%3Cul%3E%3Cli%3EBachelor’s degree in Computer Science, Engineering, Information Systems, or related technical field.%3C/li%3E%3Cli%3E8+ years of software engineering, ML engineering, platform engineering, or AI engineering experience.%3C/li%3E%3Cli%3EExperience deploying AI or ML capabilities into production environments.%3C/li%3E%3Cli%3EExperience designing scalable enterprise integration solutions.%3C/li%3E%3Cli%3EStrong understanding of operational support and production delivery.%3C/li%3E%3Cli%3EStrong communication and stakeholder management skills.%3Cbr%3E%3Cbr%3E%26nbsp;%3C/li%3E%3C/ul%3E%3Cp class=%22MsoNormal%22%3E%3Cstrong%3EPreferred Experience%3C/strong%3E%3C/p%3E%3Cul%3E%3Cli%3EMaster’s degree in Computer Science, Engineering, AI/ML, or related field.%3C/li%3E%3Cli%3EExperience building reusable engineering frameworks and productization standards.%3C/li%3E%3Cli%3EExperience operating in regulated or compliance-sensitive environments.%3C/li%3E%3Cli%3EFamiliarity with Responsible AI and AI governance practices.%3Cbr%3E%3Cbr%3E%26nbsp;%3C/li%3E%3C/ul%3E%3Cp class=%22MsoNormal%22%3E%3Cstrong%3EAdditional Information%3C/strong%3E%3C/p%3E%3Cul%3E%3Cli%3EPosition requires regular collaboration with business, technology, and external partner teams across multiple time zones.%3C/li%3E%3Cli%3ESome travel required for team, partner, and business engagements.%3C/li%3E%3Cli%3EThis role is part of MBUSA%27s Data Insights %26amp; AI organization and contributes to the company%27s long-term AI strategy and operating model.%3C/li%3E%3C/ul%3E%3Cp class=%22MsoNormal%22 style=%22margin-left:.5in;%22%3E%26nbsp;%3C/p%3E%3Cp style=%22font-family:Arial;margin-bottom:11px;%22%3E%26nbsp;%3C/p%3E%3Cp style=%22font-family:Arial;margin-bottom:11px;%22%3E%3Cspan style=%22font-family:Calibri,sans-serif;font-size:10.0pt;%22%3E%3Cspan style=%22border:1.0pt none windowtext;line-height:107%;padding:0in;%22%3E%3Cstrong%3EEEO Statement%3C/strong%3E%3C/span%3E%3C/span%3E%3C/p%3E%3Cp style=%22font-family:Arial;%22%3E%3Cspan style=%22background-color:white;font-family:Calibri,sans-serif;font-size:10.0pt;%22%3E%3Cspan style=%22line-height:15.0pt;vertical-align:baseline;%22%3EMercedes-Benz USA is committed to fostering an inclusive environment that appreciates and leverages the diversity of our team. We provide equal employment opportunity (EEO) to all qualified applicants and employees without regard to race, color, ethnicity, gender, age, national origin, religion, marital status, veteran status, physical or other disability, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local law.%3C/span%3E%3C/span%3E%3C/p%3E

Job Details
Group: Mercedes-Benz Group AG
Organization: Mercedes-Benz USA, LLC
Primary Location: United States of America-Georgia-Atlanta
Work Locations: One Mercedes-Benz Drive, Atlanta, 30328