Machine Learning Engineer- Sr. Consultant level- AI Governance - job 4 of 21

As a Machine Learning Engineer at Visa within the AI Governance team, your responsibilities will include designing and enhancing solutions that support advanced AI and machine learning technologies. You will implement effective architectural strategies, utilize generative AI tools, and develop a Responsible AI platform that tackles emerging challenges in AI governance. Additionally, evaluating third-party solutions and prototyping new technologies will be key aspects of your role.

To be considered for the Senior Consultant Machine Learning Engineer position at Visa, you typically need a strong background in machine learning and AI, often requiring a degree in computer science, engineering, or a related field. Experience with generative AI tools, knowledge of frameworks like LangGraph and LangChain, and skills in techniques such as Retrieval-Augmented Generation (RAG) and Reinforcement Learning are highly valuable. Strong problem-solving skills and the ability to work collaboratively in a team-oriented environment are also important.

Visa's AI Governance Engineering team focuses on delivering Trustworthy AI by embedding responsibility and ethics in the development and deployment of AI technologies. This involves creating a robust framework that assesses AI systems for accuracy, transparency, fairness, and robustness. The team actively engages in monitoring and improving AI models throughout their lifecycle to ensure they are used responsibly and effectively.

In the Machine Learning Engineer role at Visa’s AI Governance team, you will work with a variety of advanced technologies including generative AI tools, architectural frameworks like LangGraph and LangChain, and APIs from major AI providers. You will also implement techniques such as Retrieval-Augmented Generation (RAG) and Fine-Tuning to drive innovation in responsible AI solutions.

The work environment for a Machine Learning Engineer at Visa is dynamic and collaborative. This hybrid position allows you the flexibility to work from home while also engaging with your team in-person on designated days. You will be part of an innovative culture that encourages continuous learning, idea sharing, and teamwork to push the boundaries of AI technology.

To design a solution for AI governance, I would begin by assessing the specific requirements and compliance standards relevant to the sector. Next, I’d focus on developing a framework that ensures accuracy and fairness in AI models, incorporating monitoring techniques to evaluate their performance. Collaboration with stakeholders is crucial, as is iterating on the solution based on feedback.

I'm experienced in using generative AI tools such as OpenAI's APIs and Llama. I leverage these tools to enhance model development efficiency, often utilizing them for data augmentation or training on diverse datasets. Implementing these tools allows for faster deployment and better model accuracy by harnessing large volumes of data effectively.

In a recent project, I was tasked with developing a model governance framework for a financial application. I implemented measures for model validation and monitoring, ensuring compliance with fairness metrics. By regularly assessing model performance and ethical implications, I ensured that the AI system was aligned with industry regulations and company values.

I stay updated on AI advancements by actively engaging in online communities, attending webinars, and reading research papers. I also follow leading AI blogs and participate in forums where professionals discuss emerging trends. This not only helps me learn but also allows me to apply cutting-edge techniques to my work.

To ensure AI models are interpretable, I prioritize using algorithms that provide insights into their decision-making process. I also document the modeling choices and use visualization tools to represent the model outputs. Educating stakeholders about the models is key, and I aim to create transparent narratives around AI systems to foster trust.

I evaluate the fairness of AI models by applying statistical methods to analyze their outputs across different demographics. Techniques such as disparate impact analysis help identify any bias present. Regular audits of model predictions and incorporating fairness metrics during model evaluation are crucial steps in ensuring equitable outcomes.

In a previous role, I collaborated with data scientists, compliance officers, and business stakeholders to develop a responsible AI framework. My role involved bridging the technical aspects with business objectives by communicating model implications clearly to non-technical team members. This teamwork was pivotal for aligning goals and achieving successful project outcomes.

Continuous learning is vital in my work, given the rapid evolution of AI technologies. I allocate time for training sessions and workshops to ensure I’m abreast of new methods and tools. This commitment not only enhances my skill set but also allows me to bring innovative ideas and solutions to the team.

When integrating new AI technologies, I approach challenges by first conducting a thorough assessment of the existing frameworks and identifying potential obstacles. I prioritize collaboration with IT and compliance teams to address concerns. Additionally, I embrace an iterative approach to implementation, allowing for adjustments based on real-time feedback.

For a Machine Learning Engineer in AI Governance, crucial skills include a strong foundation in machine learning algorithms, a grasp of ethical AI practices, and familiarity with legal regulations surrounding AI use. Additionally, excellent communication skills are essential for conveying complex technical information to diverse audiences and collaborating effectively across functions.