Director, Artificial Intelligence and Machine Learning (AI/ML) Quality Oversight

职位描述

We are seeking a highly experienced Director of Artificial Intelligence and Machine Learning (AI/ML) Quality Oversight to establish and lead the quality assurance strategy for our AI/ML initiatives, specifically within a Good Manufacturing Practice (GMP) environment. This pivotal role will be responsible for defining, implementing, and maintaining rigorous quality standards and processes across the entire AI/ML lifecycle – from model development and deployment to ongoing monitoring and governance – ensuring the reliability, accuracy, ethical compliance, and overall quality of our AI/ML solutions in this regulated context. The Director will foster trust and drive responsible innovation while adhering to the stringent requirements of GMP.

Job Responsibilities

AI/ML Quality Strategy and Oversight:

• Establish and lead the overall quality strategy and framework for all AI/ML initiatives across the organization, ensuring adherence to quality principles and standards.

• Define and implement comprehensive quality assurance processes and methodologies throughout the AI/ML lifecycle, from data acquisition and model development to deployment and monitoring.

• Serve as the primary point of contact and subject matter expert for AI/ML quality-related policies, procedures, and guidelines.

• Develop and implement training curricula and materials to support the effective rollout and adoption of AI/ML quality standards, policies, and procedures across relevant teams.

Quality Assurance in Model Development & Deployment:

• Define and oversee quality gates and validation processes for the creation and implementation of machine learning models addressing diverse business needs.

• Ensure the quality and reliability of Natural Language Processing (NLP) solutions developed for tasks including sentiment analysis and automation.

• Establish quality assurance measures for the integration of Large Language Models (LLMs) in advanced AI applications such as chatbots, language generation, and custom AI solutions.

Quality in AI Model Lifecycle Management:

• Develop and enforce quality standards for robust systems and processes across the entire AI model lifecycle, including continuous model monitoring, retraining strategies, and performance optimization.

AI Governance, Ethics, and Quality Compliance:

• Develop and implement quality checks within comprehensive frameworks for responsible and compliant AI utilization, ensuring adherence to ethical standards and regulatory requirements.

Algorithm Quality and Performance:

• Define quality metrics and oversee the crafting and optimization of algorithms for various applications, ensuring accuracy, speed, and scalability.

Quality in Predictive Analytics & Forecasting:

• Establish quality assurance processes for leveraging machine learning methodologies to analyze historical data and predict future trends, ensuring the reliability of insights for strategic decision-making.

Quality in Deep Learning Applications:

• Define quality standards and oversee the development and implementation of deep learning models for complex applications such as image and speech recognition and autonomous systems.

Quality in Reinforcement Learning for Autonomous Systems:

• Establish quality assurance measures for the deployment of reinforcement learning algorithms used to train autonomous systems for real-time decision-making.

AI Model Explainability, Interpretability, and Trust:

• Champion the implementation of quality checks for techniques ensuring the explainability and interpretability of AI models, fostering trust and understanding among both technical and non-technical stakeholders.

Basic Qualifications:

• PhD in degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field with 8+ years of progressive experience in developing and deploying machine learning models and AI solutions, with a strong emphasis on quality assurance and oversight within a Good Manufacturing Practice (GMP) environment OR

• Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field with 10+ years of progressive experience in developing and deploying machine learning models and AI solutions, with a strong emphasis on quality assurance and oversight within a Good Manufacturing Practice (GMP) environment OR

• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field with 12+ years of progressive experience in developing and deploying machine learning models and AI solutions, with a strong emphasis on quality assurance and oversight within a Good Manufacturing Practice (GMP) environment OR

• Associate’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field with 14+ years of progressive experience in developing and deploying machine learning models and AI solutions, with a strong emphasis on quality assurance and oversight within a Good Manufacturing Practice (GMP) environment OR

• High School Degree with 16+ years of progressive experience in developing and deploying machine learning models and AI solutions, with a strong emphasis on quality assurance and oversight within a Good Manufacturing Practice (GMP) environment.

Preferred Qualifications:

• Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.

• 10+ years of progressive experience in developing and deploying machine learning models and AI solutions, with a strong emphasis on quality assurance and oversight.

• Deep understanding of machine learning algorithms, statistical modeling, and data mining techniques, with a focus on quality validation.

• Hands-on experience with NLP, deep learning frameworks (e.g., TensorFlow, PyTorch), and deploying models in production environments, with a quality-centric approach.

• Strong understanding of AI ethics, governance, responsible AI practices, and their implications for quality assurance.

• Excellent communication, presentation, and interpersonal skills, with the ability to explain complex technical concepts related to AI quality to both technical and non-technical audiences.

• Experience with quality assurance methodologies applied to Large Language Models (LLMs) and their integration.

• Experience with quality assessment and validation of reinforcement learning applications for autonomous systems.

• Experience in developing and implementing quality management systems for the AI model lifecycle.

• Familiarity with quality assurance tools and best practices for cloud-based AI/ML platforms.

• Publications or presentations related to AI/ML quality or testing.

• Relevant certifications in quality assurance or AI/ML.

• Willingness to travel to domestic and international sites as required.