FCSD Tech is a new global business within Ford committed to commercial customer productivity. FCSD Tech delivers a work-ready suite of vehicles, accessories, and services for virtually every vocation, backed by technology and engineered for uptime. A true one-stop shop, offering a full portfolio of electrified and internal combustion vehicles designed to integrate seamlessly with the FCSD Tech ecosystem, helping customers' businesses thrive today and into the new era of electrification.

This is an exciting opportunity to be part of a team that will be instrumental in crafting the future of how we do business, with the commercial customer at the center of everything we do.

As a Lead AI Engineer, you should have a strong technical background and demonstrated experience in Data Science, MLOps and AI/ML Engineering. You will be responsible for the delivery of analytical, machine learning and AI solutions. Successful candidates should demonstrate business acumen, knowledge of data, and AI solutions to support key products within FCSDTech that focus on improving customer experience, increasing revenue, and improving efficiency across the business.

You will be working within a mixed skill team to deliver products for FCSD Tech. The candidate will be able to develop healthy relationships and trust with product managers, business stakeholders and peers across FCSD Tech.  

The minimum requirements we seek:

Bachelor’s Degree in related field (e.g. Data Science, Computer science, Artificial intelligence and Data science, Statistics, Applied Mathematics, IT) 5+ years of experience of analytical methods and their proper application  3+ years of experience using AI/ML, data science software (e.g., Python-based tools)  Experience using Cloud Platforms and AI Platforms.  Experience using Gen AI technologies.

Our preferred requirements: 

Working knowledge of GCP. Expertise in open-source data science technologies such as Python, R, Spark, SQL. Conceptual understanding of data pipelines and MLOps

Responsibilities:

Design, develop, and implement AI/ML models and algorithms to solve complex problems. Perform data preprocessing, cleaning, and feature engineering to prepare data for model training. Train, evaluate, and tune various machine learning models. Develop and maintain robust and efficient code using Python and relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn). Develop and maintain MLOps pipeline to monitor model performance in production and re-train as needed. Document code, experiments, and results clearly and concisely. Collaborate effectively with other engineers within the team. Ability to work within an agile development model working closely with a product manager and other engineers.

Qualifications:

Strong theoretical understanding of machine learning algorithms and techniques. Proven hands-on experience in developing and implementing AI/ML models using Python. Proficiency in using relevant libraries such as TensorFlow, PyTorch, scikit-learn, etc. Experience with data preprocessing, feature engineering, and model evaluation. Excellent problem-solving and analytical skills. Ability to work independently and as part of a team. Excellent communication and documentation skills. Specific experience with cloud computing platforms like Google Cloud, AWS, or Azure

Technical Experience:

Proficiency in Google Cloud Platform (GCP) services relevant to machine learning and AI, such as AI Platform, BigQuery, Dataflow, and Tensorflow. Strong understanding of machine learning algorithms, techniques, and frameworks, including deep learning, neural networks, and ensemble methods. Experience with building and training machine learning models using tools like TensorFlow, Keras, or PyTorch. Familiarity with any one MLOps framework such as MLflow, etc., is preferred. Familiarity with cloud-based data storage and processing technologies for handling large datasets efficiently (Tekton and Terraform is a plus +). Ability to design and implement end-to-end machine learning pipelines for data ingestion, processing, modeling, and deployment. Proficiency in programming languages such as Python for data manipulation, analysis, and model development. Experience with version control systems like GitHub for managing code repositories and collaboration. Candidate should have an understanding of Agentic AI concepts and applications. Experience with at least one Agentic AI framework such as CrewAI, AutoGen, LangGraph, OpenAI Swarm etc. is preferred. Understanding of containerization technologies like Docker for packaging machine learning models and deploying them in production. Demonstrate problem-solving skills, analytical thinking, and the ability to communicate complex technical concepts effectively. Experience in Software Engineering practices and be able to translate those practices into the AI Engineering world (is a plus +).