AI Engineering Manager

Responsibilities

Strategic Thinking & Leadership

1. Collaborate with business customers to understand business challenges and develop strategies for solving them using AI/ML and advanced analytics.
2. Help define and communicate product vision and delivery with key stakeholders.
3. Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends.

Technical Leadership

1. Support technical reviews of analytical methods & AI solutions.
2. Support the prioritization of advanced analytic research needed to advance the product’s capabilities.
3. Hands on leadership of developing analytical solutions.
4. Establish and grow our AI Engineering practice

Delivery Focus

1. Drive AI products and services end-to-end in partnership with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions.
2. Influence product direction through clear and compelling presentations to leadership.

Team Development

1. Contribute towards advancing the AI Engineering and Data Science discipline in Ford Pro through the Community of Practice, including but not limited to driving data best practices, improving analytical processes, scaling knowledge and tools, and mentoring other data scientists.
2. Promote and facilitate learning and development needs of team.
3. Support team members as part of product delivery model.

Anchor Role

1. Proficiency in Google Cloud Platform (GCP) services relevant to machine learning and AI, such as AI Platform, BigQuery, Dataflow, and Tensorflow.
2. Strong understanding of machine learning algorithms, techniques, and frameworks, including deep learning, neural networks, and ensemble methods.
3. 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.
4. Experience with building and training machine learning models using tools like TensorFlow, Keras, or PyTorch.
5. Familiarity with cloud-based data storage and processing technologies for handling large datasets efficiently.
6. Ability to design and implement end-to-end machine learning pipelines for data ingestion, processing, modeling, and deployment.
7. Proficiency in programming languages such as Python for data manipulation, analysis, and model development.
8. Experience with version control systems like Git for managing code repositories and collaboration.
9. Understanding of containerization technologies like Docker for packaging machine learning models and deploying them in production.
10. Strong problem-solving skills, analytical thinking, and the ability to communicate complex technical concepts effectively.
11. Experience with Gen AI,
12. Strong in Software Engineering practices and be able to translate those practices into the AI Engineering world.

Qualifications

Qualification :

The minimum requirements we seek:

1. Bachelor’s Degree in related field (e.g. Data Science, Predictive Analytics, Statistics, Marketing Analytics, Applied Mathematics, IT)
2. 7+ years of experience of analytical methods and their proper application
3. 7+ years of experience using AI/ML , data science software (e.g., Python-based tools)
4. Experience in leading, hiring and growing Data Science teams.
5. 4+ years of experience acting as the senior technical lead helping facilitate analytical/technical discussions on solution tradeoffs for new feature implementations.
6. 4+ years leading and managing direct reports.
7. Experience using Cloud Platforms and AI Platforms.
8. Experience using Gen AI technologies.

Our preferred requirements:

1. PhD Degree in related field (e.g., Data Science, Predictive Analytics, Machine Learning, Statistics, Applied Mathematics, Computer Science)
2. Expert level of Advanced and Predictive Analytical Methods e.g., Simulation, Design of Experiments, Genetic Algorithms, Ensemble Methods, Naïve Bayes, Neural Networks, regression, image processing, natural language processing,
3. Working knowledge of GCP.
4. Expertise in open source data science technologies such as Python, R, Spark, SQL.