Staff Machine Learning Engineer- AI Governance - job 8 of 21

As a Staff Machine Learning Engineer in AI Governance at Visa, your responsibilities will include designing, developing, and maintaining scalable AI governance services that ensure compliance with ethical standards and regulatory requirements. You will work closely with interdisciplinary teams to enhance the AI Observatory product, oversee the full lifecycle of AI models, and investigate emerging technologies for integration within Visa. Your role is instrumental in driving projects that involve building a Responsible AI platform that utilizes the latest Generative AI technologies, ensuring a trustworthy AI environment.

To qualify for the Staff Machine Learning Engineer position in AI Governance at Visa, candidates should possess a strong background in machine learning and AI technologies, particularly generative AI. A degree in computer science, data science, or a related field is typically required, along with experience working in governance and ethical AI implementation. Strong analytical skills, along with proficiency in programming languages such as Python and knowledge of ML frameworks, are essential, as is the ability to collaborate effectively with diverse teams including product managers and legal professionals.

Visa ensures responsible AI practices in the AI Governance role by implementing a robust framework that emphasizes transparency, fairness, and accountability in AI systems. The Staff Machine Learning Engineer will work on developing a Responsible AI platform that integrates advanced technologies, enabling the assessment of AI models' lifecycle and ensuring compliance with ethical standards and regulatory requirements. By collaborating with a multidisciplinary team, you will contribute to ongoing evaluations and adaptations to keep the AI solutions aligned with Visa’s governance and operational standards.

The AI Observatory product is a groundbreaking initiative at Visa aimed at providing a comprehensive inventory of ML models and AI systems, overseeing their full lifecycle to ensure compliance with governance standards. As a Staff Machine Learning Engineer, your role will be to support the development and maintenance of this product, ensuring that AI solutions are effective, fair, and trustworthy. You will be responsible for implementing architectural designs and leveraging advanced technologies to streamline the governance workflow across Visa's AI capabilities.

The Staff Machine Learning Engineer position at Visa offers a hybrid work model, allowing employees to balance between remote and office work. Employees are expected to work from the office three days a week, typically Tuesdays, Wednesdays, and Thursdays. This flexible model encourages collaboration and innovation while also supporting personal productivity and work-life balance. The prospect of engaging with teams in person while also having remote flexibility makes this role appealing to tech professionals.

In my previous role, I was involved in developing and validating various machine learning models. To ensure their accuracy, I implemented rigorous testing and validation protocols, such as cross-validation and performance metrics analysis. I also collaborated with domain experts to review the model’s outputs and make necessary adjustments to increase reliability. This systematic approach has helped me maintain high standards in model accuracy and performance.

I prioritize ethical considerations by conducting thorough risk assessments at each stage of AI development. This involves evaluating potential biases in training data, ensuring transparency in algorithms, and seeking feedback from diverse stakeholders. Collaboration with legal experts is fundamental to navigate regulatory frameworks, and I advocate for user-focused designs to prioritize fairness and inclusivity in AI applications.

Building scalable AI governance solutions requires adherence to principles such as modularity, reliability, and flexibility. Developing a microservices architecture allows for independent scalability and ease of maintenance. Additionally, incorporating robust logging and monitoring systems enables versatile tracking of performance and governance compliance, ensuring that the solutions adapt to changing requirements and maintain high-quality standards.

One challenge I faced was ensuring compliance with evolving regulations in AI governance. I addressed this by creating a proactive monitoring system that involved regular updates and reviews of regulatory guidelines. Collaborating with a cross-functional team, we developed a framework that allowed us to pivot quickly in response to new regulations, ultimately enhancing our compliance strategy and governance framework.

Collaboration is central to developing responsible AI solutions, as it brings diverse perspectives and expertise into the process. Engaging with data scientists, product managers, and compliance teams ensures that various dimensions of AI solutions, including technical viability and ethical considerations, are thoroughly examined. This collaborative approach fosters innovation while safeguarding against potential biases and governance challenges.

Absolutely! In my last position, I worked extensively with Generative AI technologies, specifically in developing models for natural language processing. I implemented advanced techniques such as transformers that enabled us to create applications capable of generating human-like text. My focus was on optimizing these models for efficiency and ethical output, ensuring they adhered to our guidelines for responsible AI use.

Post-deployment, I use a combination of performance metrics and user feedback to evaluate AI models. Key metrics include accuracy, precision, recall, and F1 scores, which provide quantitative insights into the model's performance. Additionally, engagement with end-users to gather qualitative feedback helps identify areas for improvement, ensuring continuous refinement and adaptation of the models to meet user needs and expectations.

To stay current with AI governance trends, I regularly attend industry conferences and webinars, subscribe to leading journals, and participate in professional networks. Engaging with thought leaders and following research publications allows me to grasp emerging challenges and best practices. I also actively seek opportunities to collaborate on projects that involve the latest technologies, facilitating hands-on experience with innovative governance strategies.

In the event of an AI model producing biased results, my first step would be to conduct a thorough investigation into the data and algorithms used. By identifying the sources of bias, whether from skewed training datasets or flawed algorithms, I would implement corrective measures, such as re-evaluating the training data and applying bias mitigation techniques. Continuous monitoring of the model post-correction would be essential to ensure its outputs are fair and representative.

I see the future of AI governance as increasingly integral to business strategy, especially in industries like finance. As technology evolves, the complexities of responsible AI will expand, making my role crucial in devising frameworks that not only comply with regulations but set the benchmark for ethical AI practices. I envision leveraging innovative technologies to streamline governance processes, ensuring that the pursuit of AI excellence aligns seamlessly with legal, ethical, and social standards.