AI Productization Engineer

Mercedes-Benz USA

Atlanta (GA)

On-site

USD 140,000 - 190,000

Full time

5 days ago

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Benefits offered by this job

Employee phone (possible)

Employee events

Health measures

Company pension

Commuter benefits

Flexible working hours

Employee discounts

Coaching

Employee participation

Parking

Good transport links

Barrier-free access

Childcare

Canteen, cafe

Job summary

Mercedes-Benz USA seeks an AI Productization Engineer to turn AI/ML pilots into production-ready products. You will define deployment patterns, establish readiness standards, and implement integration with enterprise systems.

The role bridges innovation and sustainable business value, partnering with data science, AI engineering, and architecture teams to drive scalable AI solutions. You will own versioning, validation gates, and performance optimizations, ensuring reliability, observability,

Qualifications

Bachelor’s degree required in a technical field.
8+ years of software/AI engineering or related experience.
Experience deploying AI or ML capabilities into production environments.
Experience designing scalable enterprise integration solutions.
Strong understanding of operational support and production delivery.
Excellent communication and stakeholder management skills.

Responsibilities

Lead the transition of AI and ML solutions from pilot to production.
Develop reusable deployment, integration, and operational frameworks.
Establish production readiness standards and supportability requirements.
Define integration patterns connecting AI capabilities with enterprise systems.
Establish model and service versioning strategies with automated validation gates.
Ensure solutions meet reliability, scalability, monitoring, and support expectations.
Drive consistency across AI delivery efforts and data validation processes.
Collaborate with data science, AI engineering, architecture, and business teams.

Skills

Python

SQL

Azure Databricks

Azure / AWS

MLflow

API development

Docker

Kubernetes

CI/CD

Production operations

Enterprise integration

Terraform

Testing frameworks (Locust)

Education

Bachelor’s degree in Computer Science, Engineering, Information Systems, or related technical field

Tools

Databricks Workflows

Airflow

Terraform

OpenTelemetry

Azure API Management / AWS API Gateway

GraphQL

gRPC

Redis

Job description

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.

Aufgaben

About Us

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.
Establish production readiness standards and supportability requirements.
Define integration patterns connecting AI capabilities with enterprise business systems.
Establish model and service versioning strategies, rollback procedures, and environment promotion workflows (dev → staging → production) with automated validation gates at each stage.
Ensure solutions meet expectations for reliability, scalability, monitoring, and support.
Drive consistency and repeatability across AI delivery efforts.
Implement data validation and input contracts for AI pipelines to detect and handle upstream data changes before impacting model outputs.

Architecture & Integration Leadership (20%)

Define reference architectures and integration standards for AI products.
Partner with engineering teams to accelerate solution deployment and adoption.
Evaluate productization technologies, tooling, and engineering approaches.
Contribute to architecture reviews and technical planning.
Define API contracts for AI products, covering versioning, deprecation, rate limits, quotas, throttling, and SDK guidance.
Define caching and performance strategies for production AI serving, including result caching, request deduplication, and edge optimizations for low latency.

Operational Excellence (10%)

Develop standards for monitoring, incident response, deployment governance, and sustainment.
Establish operational documentation and engineering best practices.
Drive continuous improvement in production support processes.
Define AI incident management processes, including classification, escalation, post-incident reviews, and handling of model-specific failures like degradation, hallucinations, and data poisoning.
Develop AI product DR/BC plans, including failover strategies, RTO/RPO targets, and fallback modes (e.g., rules-based logic).
Implement audit logging and traceability for AI decisions, including inference logging, input/output capture, and end to end data lineage.

Collaboration & Technical Leadership (10%)

Collaborate with data science, AI engineering, architecture, and business teams.
Provide technical mentorship and guidance to engineering teams.
Promote engineering excellence and sustainable delivery practices.
Provide technical mentorship and guidance across the AI Engineering organization.
Support knowledge sharing, cross-training, and engineering excellence initiatives.

Qualifikationen

Technical Skills & Tools

Required

Python and SQL.
Azure Databricks and enterprise AI platforms.
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

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

Position requires regular collaboration with business, technology, and external partner teams across multiple time zones.
Some travel required for team, partner, and business engagements.
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.

Benefits