As a Principal AI Engineer, he will be part of a high performing team working on exciting opportunities in AI within Ford Credit. We are looking for a highly skilled, technical, hands-on AI engineer with a solid background in building end-to-end AI applications, exhibiting a strong aptitude for learning and keeping up with the latest advances in AIDevelop Machine Learning (Supervised/Unsupervised learning), Neural Networks (ANN, CNN, RNN, LSTM, Decision tree, Encoder, Decoder), Natural Language Processing, Generative AI (LLMs, Lang Chain, RAG, Vector Database) . He should be able to lead technical discussion and technical mentor for the team.

Professional Experience:

Potential candidates should possess 10+  years of strong working experience in AI. BE/MSc/ MTech /ME/PhD (Computer Science/Maths, Statistics). Possess a strong analytical mindset and be very comfortable with data. Experience with handling both relational and non-relational data. Hands-on experience with analytics methods (descriptive/predictive/prescriptive), Statistical Analysis, Probability and Data Visualization tools (Python-Matplotlib, Seaborn).  Background of Software engineering with excellent Data Science working experience.

Technical Experience:

Develop Machine Learning (Supervised/Unsupervised learning), Neural Networks (ANN, CNN, RNN, LSTM, Decision tree, Encoder, Decoder), Natural Language Processing, Generative AI (LLMs, Lang Chain, RAG, Vector Database) . Excellent in communication and presentation skills. Ability to do stakeholder management. Ability to collaborate with a cross-functional team involving data engineers, solution architects, application engineers, and product teams across time zones to develop data and model pipelines. Ability to drive and mentor the team technically, leveraging cutting edge AI and Machine Learning principles and develop production-ready AI solutions.  Mentor the team of data scientists and assume responsible for the delivery of use cases. Ability to scope the problem statement, data preparation, training and making the AI model production ready. Work with business partners to understand the problem statement, translate the same into analytical problem. Ability to manipulate structured and unstructured data. Develop, test and improve existing machine learning models. Analyse large and complex data sets to derive valuable insights. Research and implement best practices to enhance existing machine learning infrastructure. Develop prototypes for future exploration. Design and evaluate approaches for handling large volume of real data streams. Ability to determine appropriate analytical methods to be used. Understanding of statistics and hypothesis testing.