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.
