AI Engineering Manager
Ford Motor Company
- Location
- Chennai, Tamil Nadu, India
- Job type
- Full-time
Required skills
- BigQuery
- cloud infrastructure
- compliance
- computer vision
- containerization
- cross-functional
- data science
- Dataflow
- deep learning
- Docker
- end-to-end
- GCP
- genetic algorithms
- Git
- Google Cloud
- Keras
- machine learning
- NLP
- predictive analytics
- product delivery
- regression
- Spark
- SQL
- statistics
- team development
- TensorFlow
- ROI analysis
- Pytorch
- Vertex
About the role
Ford Motor Company
Website:
ford.com
Job details:
Job Description
Strategic Thinking & Leadership
- Partner with business leaders to identify high-impact AI opportunities and translate them into scalable AI/ML solutions.
- Define and communicate AI product vision, roadmaps, and measurable success metrics.
- Drive AI strategy across predictive analytics, Generative AI, and intelligent automation initiatives.
- Establish governance frameworks for Responsible AI, model explainability, fairness, and compliance.
- Lead cross-functional AI programs and influence executive stakeholders through compelling insights and presentations.
Technical Leadership & Expertise
- Architect and oversee end-to-end AI/ML and GenAI systems, including:
- Predictive analytics models
- Deep learning and neural networks
- NLP and computer vision solutions
- Retrieval-Augmented Generation (RAG) systems
- Agentic AI frameworks and multi-agent orchestration systems
- Strong proficiency in Google Cloud Platform (GCP) services for AI/ML (Vertex AI, BigQuery, Dataflow, Cloud Storage)
- Deep expertise in machine learning algorithms including ensemble methods, neural networks, regression models, simulation and optimization techniques, NLP, and image processing
- Experience building AI systems using TensorFlow, PyTorch, Keras, and Python-based ecosystems
- Experience with LLMs, foundation models, prompt engineering, fine-tuning, and evaluation pipelines
- Implement scalable MLOps and LLMOps practices including CI/CD for ML, model versioning, monitoring, and automated retraining
- Proficiency in Git, Docker, API-based deployments, and scalable cloud AI services
- Apply strong software engineering practices within AI systems including testing, modular design, observability, and documentation
- Drive research and innovation in advanced AI techniques to enhance enterprise capabilities
- Support architectural reviews and ensure best practices across AI systems
- Implement Responsible AI principles including governance, model explainability, fairness, and ethical AI compliance
Delivery Focus
- Own end-to-end AI product delivery in partnership with Product, Engineering, and Data teams.
- Ensure production-grade deployment of AI models using containerization (Docker), orchestration, and scalable cloud infrastructure.
- Influence investment decisions using measurable impact metrics and ROI analysis.
- Establish monitoring frameworks for model drift, performance degradation, and system reliability.
Team Development & Community Leadership
- Lead and mentor AI engineers and data scientists.
- Build AI engineering standards, reusable frameworks, and shared tooling across SSDA.
- Promote knowledge sharing through Communities of Practice.
- Foster a culture of experimentation, continuous learning, and engineering excellence.
- Support talent development in emerging AI domains including GenAI and agent-based systems.
Responsibilities
Strategic Thinking & Leadership
- Partner with business leaders to identify high-impact AI opportunities and translate them into scalable AI/ML solutions.
- Define and communicate AI product vision, roadmaps, and measurable success metrics.
- Drive AI strategy across predictive analytics, Generative AI, and intelligent automation initiatives.
- Establish governance frameworks for Responsible AI, model explainability, fairness, and compliance.
- Lead cross-functional AI programs and influence executive stakeholders through compelling insights and presentations.
Technical Leadership & Expertise
- Architect and oversee end-to-end AI/ML and GenAI systems, including:
- Predictive analytics models
- Deep learning and neural networks
- NLP and computer vision solutions
- Retrieval-Augmented Generation (RAG) systems
- Agentic AI frameworks and multi-agent orchestration systems
- Strong proficiency in Google Cloud Platform (GCP) services for AI/ML (Vertex AI, BigQuery, Dataflow, Cloud Storage)
- Deep expertise in machine learning algorithms including ensemble methods, neural networks, regression models, simulation and optimization techniques, NLP, and image processing
- Experience building AI systems using TensorFlow, PyTorch, Keras, and Python-based ecosystems
- Experience with LLMs, foundation models, prompt engineering, fine-tuning, and evaluation pipelines
- Implement scalable MLOps and LLMOps practices including CI/CD for ML, model versioning, monitoring, and automated retraining
- Proficiency in Git, Docker, API-based deployments, and scalable cloud AI services
- Apply strong software engineering practices within AI systems including testing, modular design, observability, and documentation
- Drive research and innovation in advanced AI techniques to enhance enterprise capabilities
- Support architectural reviews and ensure best practices across AI systems
- Implement Responsible AI principles including governance, model explainability, fairness, and ethical AI compliance
Delivery Focus
- Own end-to-end AI product delivery in partnership with Product, Engineering, and Data teams.
- Ensure production-grade deployment of AI models using containerization (Docker), orchestration, and scalable cloud infrastructure.
- Influence investment decisions using measurable impact metrics and ROI analysis.
- Establish monitoring frameworks for model drift, performance degradation, and system reliability.
Team Development & Community Leadership
- Lead and mentor AI engineers and data scientists.
- Build AI engineering standards, reusable frameworks, and shared tooling across SSDA.
- Promote knowledge sharing through Communities of Practice.
- Foster a culture of experimentation, continuous learning, and engineering excellence.
- Support talent development in emerging AI domains including GenAI and agent-based systems.
Qualifications
Minimum Requirements
- Bachelor’s Degree in a related field (Data Science, Machine Learning, Computer Science, Statistics, Applied Mathematics, IT, or equivalent).
- 5 to 8 years of experience applying analytical methods and AI/ML solutions in enterprise environments.
- 5 to 8 years of experience using Python-based AI/ML technologies.
- Experience leading AI or Data Science teams.
- Experience acting as a senior technical lead facilitating solution trade-offs and architectural decisions.
- Experience using Cloud AI Platforms (GCP preferred).
- Hands-on experience with Generative AI technologies and enterprise AI deployment.
Preferred Requirements
- Master’s or PhD in Data Science, Machine Learning, Statistics, Applied Mathematics, or Computer Science.
- Experience managing and growing high-performing AI teams.
- Expert-level knowledge in advanced predictive analytics and AI techniques (Genetic Algorithms, Ensemble Learning, Neural Networks, NLP, Simulation, Design of Experiments).
- Strong working knowledge of GCP and enterprise AI architecture patterns.
- Expertise in open-source technologies such as Python, R, Spark, SQL.
- Experience building enterprise-grade GenAI and agent-based AI solutions.
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