Bachelor s degree in computer science, Engineering, Information Systems, or a closely related quantitative field.
Minimum of 10 years of progressive experience in AI/ML engineering, platform architecture, or technical leadership within a large-scale enterprise environment.
Minimum of 3+ years of direct, hands-on experience leading the design, implementation, and operationalization of enterprise AI platforms.
Proven experience in building, deploying, and operating AI/ML solutions on at least one major cloud provider (e.g., Microsoft Azure, Google Cloud Platform, or Databricks).

Technical Expertise

Deep and practical understanding of AI/ML platform architectural patterns, including model lifecycle management, MLOps, and responsible AI.
Mastery of data and model pipeline design, orchestration, and monitoring.
Expertise in designing, building, and optimizing robust, scalable, and fault-tolerant AI/ML pipelines using cloud-native services.
Strong proficiency in at least one relevant programming language for AI/ML (e.g., Python, R, Scala).
Demonstrable experience with Infrastructure-as-Code (IaC) tools (e.g., Terraform, ARM templates, Cloud Deployment Manager) for automating deployment and management of platform resources.
Solid understanding of AI governance concepts (e.g., model explainability, bias detection, auditability) and their implementation within an enterprise context.
Comprehensive knowledge of data and model security best practices for cloud AI platforms, including access control, encryption, and privacy regulations.

Leadership Strategic Acumen

Proven experience in leading, mentoring, and developing high-performing technical teams (e.g., AI/ML engineers, platform engineers).
Exceptional verbal and written communication skills, with the ability to clearly articulate complex technical concepts, strategic visions, and business value to diverse audiences.
Demonstrated ability to drive organizational change and influence key stakeholders without direct authority.
Strong analytical, problem-solving, and critical thinking abilities, with a track record of successfully resolving complex technical challenges.
Experience in facilitating cross-functional collaboration between data science, engineering, governance, and business units.

Preferred Qualifications

Master s degree or higher in Computer Science, Data Science, Engineering, or a related field.
Certifications in AI/ML, cloud platforms, or MLOps (e.g., Azure AI Engineer, Google Professional ML Engineer, Databricks certifications).
Experience with AI governance and cataloging tools (e.g., Azure Purview, Google Data Catalog, Databricks Unity Catalog).
Familiarity with DevOps and MLOps practices, including CI/CD pipelines for AI/ML products and model deployment.
Experience with real-time data processing and streaming technologies.
Knowledge of data visualization and AI product consumption tools (e.g., Power BI, Tableau, Looker).
Prior experience in the pharmaceutical or life sciences industry, understanding relevant data privacy and regulatory compliance (e.g., GxP, HIPAA, GDPR).
Experience with cost optimization strategies for large-scale cloud AI platforms.
Active participation in the AI/ML or platform engineering community (e.g., speaking engagements, open-source contributions, blog posts).

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