Generative AI Developer

At Visteon, the work we do is both relevant and recognized—not just by our organization, but by our peers, by industry-leading brands, and by millions of drivers around the world. That's YOUR work. And, as a truly global technology leader in the mobility space, focused on building cross-functional AND cross-cultural teams, we connect you with people who help you grow. So here, whatever we do is not a job. It's a mission. As a multi-billion-dollar leader of disruptive change in the industry, we are shaping the future, while enabling a cleaner environment. No other industry offers more fast-paced change and opportunity. We are in the midst of a mobility revolution that will completely change the way we interact with our vehicles, reduce the number of car accidents and fatalities, and make the world a cleaner place. Visteon is at the epicenter of this mobility revolution. Two major trends in the automotive industry – the shift to electric vehicles and vehicles with autonomous safety technologies – have created unique opportunities for Visteon. We are the only automotive provider focused exclusively on cockpit electronics – the fastest-growing segment in the industry. Mission of the Role : Facilitate Enterprise machine learning and artificial intelligence solutions using the latest technologies Visteon is adopting globally. Key Objectives of this Role : The primary goal of the Global ML / AI Developer is to leverage advanced machine learning and artificial intelligence techniques to develop innovative solutions that drive Visteon's strategic initiatives. By collaborating with cross-functional teams and stakeholders, this role identifies opportunities for AI-driven improvements, designs and implements scalable ML models, and integrates these models into existing systems to enhance operational efficiency. Following development best practices, fostering a culture of continuous learning, and staying abreast of AI advancements, the Global ML / AI Developer ensures that all AI solutions align with organizational goals, support data-driven decision-making, and continuously improve Visteon's technological capabilities.

Key Performance Indicators :

• Model Accuracy and Performance : Track deployed ML models' accuracy, precision, recall, and F1 scores to ensure they meet performance benchmarks and deliver reliable predictions.
• Deployment Efficiency : Measure the time taken to deploy ML models from development to production, ensuring a streamlined and efficient deployment process.
• Scalability and Integration : Evaluate the scalability of ML models by monitoring their performance under varying loads and their integration with existing systems. KPIs could include the number of integrated data sources and the efficiency of data pipelines.
• Innovation and Research : Track the adoption of new ML / AI techniques and technologies, participation in industry conferences, and contributions to research publications or internal knowledge-sharing platforms.
• Model Maintenance and Updates : Monitor the frequency and effectiveness of model updates and maintenance activities to ensure that models remain accurate and relevant.
• Training and Knowledge Transfer : Assess the effectiveness of training programs and knowledge transfer by tracking team members' proficiency in ML / AI development and the adoption of best practices.
• Business Impact : Measure the impact of ML / AI solutions on business outcomes, such as increased revenue, cost savings, or improved operational efficiency. Use metrics like ROI (Return on Investment) or specific business KPIs to evaluate success.
• Compliance and Ethics : Ensure compliance with data privacy regulations and ethical standards by monitoring adherence to data governance policies, bias mitigation strategies, and transparency in model decision-making processes.
• Customer Satisfaction : Collect feedback from internal stakeholders or clients to evaluate their satisfaction with ML / AI solutions. Use metrics like Net Promoter Score (NPS) or customer satisfaction surveys to identify areas for improvement.

Key Year One Deliverables :

Qualification, Experience and Skills :

Technical Skills :

Skills Required

training and deploying machine learning models, data engineering concepts, core machine learning algorithms