Ford Motor Company · 5 days ago

Sr. Machine Learning Architect

United States

Full-time

Remote

Senior Level, Lead/Staff

10+ years exp

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AutomotiveAutonomous Vehicles

No H1B

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Responsibilities

The Sr. ML Architect is expected to work closely with other stakeholders (ML Engineers, Data Scientists, and Data Engineers) with key responsibilities to:

Develop an in-depth understanding of the current Risk Analytics landscape and lead the Future State Architecture for the new Risk Platform in GCP.

Lead evaluation of design options, tools, and utilities to build implementation patterns for MLOps using SAS Viya and VertexAI in the most optimal ways.

Create solutions and conduct hands-on PoCs to move Risk Platform Components (KBS, CRIF, and SAS) to GCP.

Work with Suppliers, Google Professional Services, and other Consultants as required to migrate key Risk models to GCP for Lending and Insurance platforms

Collaborate with program managers to plan iterations, backlogs, and dependencies across all workstreams to progress the program at the required pace.

Led a team of ML Architects to guide Design/Architecture/Tool/Implementation Patterns, Troubleshooting Guidance, and Remove Roadblocks for the Engineering teams.

Drive adoption of standardized architecture, design, and quality assurance approaches across all workstreams and ensure migrated solutions adhere to established standards

Provide advisory and technical consulting across all initiatives including product evaluations and recommendations, security, architecture assessments, integration considerations, etc.

Collaborate with Data Engineering architects, SMEs, and technical leads to establish best practices for data products needed for model training and monitoring considering regulatory policy and legal compliance.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Google Cloud PlatformRisk AnalyticsMLOpsArchitecture Design PatternsDevOps PrinciplesCloud Security Best PracticesMicroservices ArchitectureGoogle Professional Cloud Architect CertificationMachine Learning Engineer Certification

Required

Bachelor’s degree in computer science or related field

10+ years of relevant work experience in solution, application, and data architecture, with deep understanding of cloud hosting concepts and implementations.

5+ years of hands-on experience in Risk Analytics, MLOps and Architecting Solutions for ML based models and Rules Engines

Experience in migrating legacy applications to Cloud platform and business adoption of these platforms

Knowledge of enterprise frameworks and technologies. Strong in architecture design patterns, experience with secure interoperability standards and methods, architecture tools and processes.

Strong understanding on DevOps principles and practices, including continuous integration and deployment (CI/CD), automated testing & deployment pipelines.

Good understanding of cloud security best practices and be familiar with different security tools and techniques like Identity and Access Management (IAM), Encryption, Network Security, etc.

Understanding of microservices architecture

Ability to negotiate & influence key stakeholders

Strong leadership, communication, interpersonal, organizing, and problem-solving skills.