Global Gen AI Developer Enabling a software-defined electrified future Visteon is a technology company that develops and builds innovative digital cockpit and electrification products at the leading-edge of the mobility revolution Founded in 2000 Visteon brings decades of automotive intelligence combined with Silicon Valley speed to apply global insights that help transform the software-defined vehicle of the future for many of the world s largest OEMs The company employs 10 000 employees in 18 countries around the globe 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 creative 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 Current State Assessment and Documentation Conduct a thorough evaluation of the existing AI ML infrastructure including data sources models platforms and governance practices Document findings and identify areas for improvement Stakeholder Needs Analysis Collaborate with key stakeholders to understand their requirements challenges and priorities for AI ML solutions Gain a comprehensive understanding of business objectives and user needs Roadmap and Strategy Development Develop a strategic roadmap for AI ML implementation that aligns with organizational goals Prioritize initiatives based on business value feasibility data availability technical constraints and resource needs Prototyping and Proof of Concept Create prototypes or proof of concept solutions to demonstrate the potential of AI ML in addressing specific business challenges or opportunities Validate technical feasibility and gather stakeholder feedback Governance Framework Establishment Establish and implement a governance framework for AI ML development and usage including data security policies access controls data quality standards and change management processes Training and Enablement Programs Design and deliver training programs to enhance the skills and capabilities of team members and end users in AI ML development best practices and data analysis techniques Model Development and Deployment Develop and deploy high-quality AI ML models to meet critical business requirements Ensure models are accurate scalable and aligned with user needs Integration with Existing Systems Integrate AI ML solutions with existing data sources applications and systems to create a unified analytics environment Ensure seamless data flow and interoperability between AI ML models and other platforms Documentation and Knowledge Sharing Document AI ML solutions best practices and lessons learned to facilitate knowledge sharing and promote reusability across the organization Performance Measurement and Reporting Establish performance metrics and reporting mechanisms to track progress against key objectives and KPIs Provide regular updates to stakeholders on the status of AI ML initiatives and their impact on business outcomes Qualification Experience and Skills Technical Skills Expertise in machine learning frameworks e g TensorFlow PyTorch programming languages e g Python R SQL and data processing tools e g Apache Spark Hadoop Proficiency in developing training and deploying ML models including supervised and unsupervised learning deep learning and reinforcement learning Strong understanding of data engineering concepts including data preprocessing feature engineering and data pipeline development Experience with cloud platforms preferably Microsoft Azure for deploying and scaling ML solutions Business Acumen Strong business analysis and ability to translate complex technical concepts into actionable business insights and recommendations Key Behaviors Innovation Continuously seeks out new ideas technologies and methodologies to improve AI ML solutions and drive the organization forward Attention to Detail Pays close attention to all aspects of the work ensuring accuracy and thoroughness in data analysis model development and documentation Effective Communication Clearly and effectively communicates complex technical concepts to non-technical stakeholders ensuring understanding and alignment across the organization Reporting Structure This role reports to the Innovation department lead Visteon Culture If you thrive in a quick-paced organizational culture that requires agility adaptability and a growth mindset from its employees to thrive and stay ahead of the curve Visteon is the place We value high performance and a drive for results Innovation risk-taking and continuous learning help us keep up with the ever-changing landscape of our industry and be Market leaders At Visteon you can be more Job Snapshot Updated Date 23-Oct-2025 Job ID J10315 Job Discipline IT Development Location VTSC - Kolkata Kolkata West Bengal India Employee Type Full Time Employees

📌 Global Gen Ai Developer (West Bengal)
🏢 Visteon
📍 West Bengal