Machine Learning Engineer

This is a great opportunity to drive the delivery of a key enterprise objective in building Ford’s flagship products - bring Innovation in Manufacturing to have significant business impact. In this role you will design, develop, and deploy scalable and robust machine learning pipelines and models in a production environment. You will collaborate with data scientists and engineers to build end-to-end ML solutions, focusing on automation, performance, and reliability. You will implement MLOps best practices and contribute to the continuous improvement of Ford's ML infrastructure. You also get to work with a unique blend of engineers, DevOps, automation, controls, manufacturing and robotics specialists - ideating, building and scaling Billion-Dollar ideas for the manufacturing of iconic Ford products. This position requires an individual who is at the forefront of AI and Machine Learning technologies and believes in bringing the latest and greatest to Ford’s plant floor to build impactful use cases that can be industrialized with the latest technologies. This is a rare opportunity to put your signature on how Ford manufactures vehicles.Minimum Qualifications- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Electrical Engineering, or a related technical field.- 5+ years of experience building and deploying machine learning systems in production environments.- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, scikit-learn, or XGBoost.- Experience in machine learning model development, deployment, and maintenance- Expertise in big data processing/movement and architecting the data governance- Rest API/Endpoint creation for batch inferencing /real-time inferencing of the ML model- Solid understanding of model evaluation, performance tuning, and error analysis techniques.- Familiarity with deploying ML to cloud platforms (e.g., Azure, AWS, GCP) and/or edge environments.- Strong problem-solving, system design, and software engineering skills with a bias for operational impact.- Expertise in implementation reusable components based on Pytorch, Tensorflow-V1, 2 for deep learning, ANN, hybrid modeling Optimization local/global for improving the accuracy of the ML/AI models- Knowledge of different orchestration tools like Airflow, Kubeflow pipelines- Ability to work independently and as part of a team- Strong attention to detail and ability to deliver high-quality work in a fast-paced environment.Preferred Qualifications- Hands-on experience with GenAI or large language models (e.g., OpenAI, Hugging Face, Azure OpenAI) in operational use cases.- Familiarity with time series forecasting, computer vision, anomaly detection, or reinforcement learning in real-time systems.- Experience with hybrid deployment models—cloud, edge, and on-premises—with considerations for latency, data privacy, and reliability.- Knowledge of Data-Centric Architecture (DCA) and reusable ML/AI asset strategies (features, models, evaluations).- Ability to collaborate deeply with cross-functional stakeholders to translate industrial challenges into scalable ML solutions.- Strong knowledge of software engineering best practices, including version control, testing, and debugging.- Design, build, and deploy machine learning solutions that solve complex problems in manufacturing—ranging from predictive maintenance and quality control to GenAI-driven process intelligence.- Industrialize machine learning workflows—feature engineering, model training, evaluation, and continuous learning—in a production environment.- Develop intelligent agents or copilots using foundation models (e.g., GenAI) to enhance plant operations, operator support, or engineering diagnostics.- Ensure robustness, scalability, and real-time performance of ML models deployed in hybrid environments (edge/cloud).- Maintain and improve MLOps pipelines, including model versioning, monitoring, retraining, and automated deployment.- Contribute to architecture and platform decisions supporting a Data-Centric Architecture (DCA) that treats models as first-class, reusable data assets.- Work with cross-functional teams to gather requirements and provide solutions to complex problems- Stay up to date with the latest developments in machine learning and related technologies and incorporate them into the company's systems- Collaborate with the software engineers, full stack developers/ data engineers/ architects/ product owners/ product managers and work on engineering/ architecting scalable, reliable, and modular ML solutions into production environments.