AI Productization Engineer
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Before you apply
LocationUnited States of America-Georgia-Atlanta
Typefull-time
SenioritySenior
Experience8+ yrs
DepartmentData Insights & AI
Company size10,000+ people
First seenOct 22, 2026 · 2w ago
Verified live5d ago
At a glanceSummarised by Seekless from the posting.
Bachelor's degree in Computer Science, Engineering, Information Systems, or related technical field
8+ years of software engineering, ML engineering, platform engineering, or AI engineering experience
Experience deploying AI or ML capabilities into production environments
Experience designing scalable enterprise integration solutions
Strong understanding of operational support and production delivery
Strong communication and stakeholder management skills
Proficiency in Python
Proficiency in SQL
Proficiency in Azure Databricks
Proficiency in Azure cloud platform
Proficiency in AWS cloud platform
Proficiency in MLflow
Proficiency in API development
Proficiency in enterprise integration patterns
Proficiency in Docker
Proficiency in Kubernetes
Proficiency in CI/CD pipelines
Proficiency in production operations
Proficiency in observability
Proficiency in monitoring
Enterprise application integration experience
Proficiency in Terraform or equivalent infrastructure as code
Proficiency in testing frameworks for AI systems (integration testing, performance/load testing)
Proficiency in Locust for load testing
Master's degree in Computer Science, Engineering, AI/ML, or related field
Experience building reusable engineering frameworks and productization standards
Experience operating in regulated or compliance-sensitive environments
Familiarity with Responsible AI and AI governance practices
Proficiency in Salesforce integration
Proficiency in ServiceNow integration
Proficiency in SAP integration
Proficiency in Azure API Management or AWS API Gateway
Proficiency in Apache Airflow
Proficiency in Databricks Workflows
Proficiency in OpenTelemetry
Experience supporting AI and agent-based solutions in production
Proficiency in caching technologies (Redis, Azure Cache, CDN)
Proficiency in Responsible AI tooling (SHAP, LIME, Azure Responsible AI)
Proficiency in modern API patterns (GraphQL, gRPC, WebSockets)
Skills
Python
SQL
Azure Databricks
Azure
AWS
MLflow
Docker
Kubernetes
CI/CD
Terraform
Locust
Salesforce
ServiceNow
SAP
Azure API Management
AWS API Gateway
Apache Airflow
Databricks Workflows
OpenTelemetry
Redis
Azure Cache
CDN
SHAP
LIME
Azure Responsible AI
GraphQL
gRPC
WebSockets
About Us
Mercedes-Benz USA is responsible for the sales, marketing and service of all Mercedes-Benz and Maybach products in the United States. In our people, you will find tremendous commitment to our corporate values: ‘PRIDE = Passion, Respect, Integrity, Discipline, and Execution’. Our products and employees reflect this dedication. We are looking for diverse top-notch individuals to join the Mercedes-Benz Team and uphold these hallmarks.
Job Overview
The 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.
The 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.
Responsibilities
AI Productization & Production Readiness (60%)
•
Lead the transition of AI and machine learning solutions from pilot to production.
•
Develop reusable deployment, integration, and operational frameworks.
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Establish production readiness standards and supportability requirements.
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Define integration patterns connecting AI capabilities with enterprise business systems.
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Establish model and service versioning strategies, rollback procedures, and environment promotion workflows (dev → staging → production) with automated validation gates at each stage.
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Ensure solutions meet expectations for reliability, scalability, monitoring, and support.
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Drive consistency and repeatability across AI delivery efforts.
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Implement data validation and input contracts for AI pipelines to detect and handle upstream data changes before impacting model outputs.
Architecture & Integration Leadership (20%)
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Define reference architectures and integration standards for AI products.
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Partner with engineering teams to accelerate solution deployment and adoption.
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Evaluate productization technologies, tooling, and engineering approaches.
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Contribute to architecture reviews and technical planning.
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Define API contracts for AI products, covering versioning, deprecation, rate limits, quotas, throttling, and SDK guidance.
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Define caching and performance strategies for production AI serving, including result caching, request deduplication, and edge optimizations for low latency.
Operational Excellence (10%)
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Develop standards for monitoring, incident response, deployment governance, and sustainment.
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Establish operational documentation and engineering best practices.
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Drive continuous improvement in production support processes.
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Define AI incident management processes, including classification, escalation, post-incident reviews, and handling of model-specific failures like degradation, hallucinations, and data poisoning.
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Develop AI product DR/BC plans, including failover strategies, RTO/RPO targets, and fallback modes (e.g., rules-based logic).
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Implement audit logging and traceability for AI decisions, including inference logging, input/output capture, and end to end data lineage.
Collaboration & Technical Leadership (10%)
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Collaborate with data science, AI engineering, architecture, and business teams.
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Provide technical mentorship and guidance to engineering teams.
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Promote engineering excellence and sustainable delivery practices.
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Provide technical mentorship and guidance across the AI Engineering organization.
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Support knowledge sharing, cross-training, and engineering excellence initiatives.
Technical Skills & Tools
Required
•
Python and SQL.
•
Azure Databricks and enterprise AI platforms.
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Azure or AWS cloud platforms.
•
MLflow and MLOps tooling.
•
API development and enterprise integration patterns.
•
Docker, Kubernetes, and CI/CD pipelines.
•
Production operations, observability, and monitoring.
•
Enterprise application integration experience.
•
Infrastructure as code (Terraform or equivalent)
•
Testing frameworks and strategies for AI systems, including integration testing, performance/load testing (Locust or equivalent),
Preferred Skillset
•
Salesforce integration.
•
ServiceNow integration.
•
SAP integration.
•
Azure API Management or AWS API Gateway.
•
Apache Airflow or Databricks Workflows.
•
OpenTelemetry and advanced observability tooling.
•
Experience supporting AI and agent-based solutions in production.
•
Caching technologies (Redis, Azure Cache, CDN)
•
Responsible AI tooling for production, including fairness monitoring, bias detection, and
explainability frameworks (e.g., SHAP, LIME, Azure Responsible AI).
•
Modern API patterns (GraphQL, gRPC, WebSockets) for high-performance and real-time AI product interfaces.
Qualifications
Required
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Bachelor’s degree in Computer Science, Engineering, Information Systems, or related technical field.
•
8+ years of software engineering, ML engineering, platform engineering, or AI engineering experience.
•
Experience deploying AI or ML capabilities into production environments.
•
Experience designing scalable enterprise integration solutions.
•
Strong understanding of operational support and production delivery.
•
Strong communication and stakeholder management skills.
Preferred Experience
•
Master’s degree in Computer Science, Engineering, AI/ML, or related field.
•
Experience building reusable engineering frameworks and productization standards.
•
Experience operating in regulated or compliance-sensitive environments.
•
Familiarity with Responsible AI and AI governance practices.
Additional Information
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Position requires regular collaboration with business, technology, and external partner teams across multiple time zones.
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Some travel required for team, partner, and business engagements.
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This role is part of MBUSA’s Data Insights & AI organization and contributes to the company’s long-term AI strategy and operating model.
EEO Statement
Mercedes-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.