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Ford Motor Company

AI Engineering Manager

Ford Motor Company
Chennai, India
5-8 Years
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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.

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.

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.

More Info

Job Type:
Permanent Job
Industry:
Other
Function:
Ai/Ml
Employment Type:
Full time

About Company

Ford Motor Company

Job ID: 148621163

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Last Updated: 02-06-2026 05:10:02 PM
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