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AI Enterprise Architect

Ford Global902 open roles

Where
Chennai, Tamil Nadu, India
Work mode
On site
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Your applicationOpen nowAI Enterprise ArchitectFord Global · Chennai, Tamil Nadu, India

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Ford Global's own form.

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The clock on this job

Early applications get read.

13.4% of postings close within 7 days. Measured by our own scanner across the market. Ford Global postings stay open a median of 10 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.6%3 days
  3. 13.4%7 days
  4. 21.6%14 days
  5. 36.2%30 days
  6. 59.0%60 days
This job: posted today

Ford Global median: 10 days open

The posting

Job Description

As Ford accelerates its enterprise-wide digital and AI transformation, the AI Advancement Center is expanding its ability to design, build and deploy secure, high-performing AI platforms that can be reused across business functions and geographies.

As a Principal AI Architect, you will define the architectural vision and technical standards for enterprise AI platforms, connecting strategic business requirements with engineering execution.

You will work across engineering, data, cloud, cybersecurity and business teams to establish scalable patterns for Machine Learning Operations (MLOps), Generative AI, Retrieval-Augmented Generation (RAG), model serving, data processing and AI governance.

This is a senior technical leadership role with significant enterprise influence. Your work will help Ford accelerate AI adoption while improving platform reliability, security, reusability and cost efficiency.

Responsibilities

Enterprise AI Architecture

  • Define end-to-end architecture for enterprise-scale AI, ML and Generative AI platforms across cloud and distributed environments.
  • Establish reusable architectural patterns for Retrieval-Augmented Generation (RAG), model fine-tuning, inference, model serving, vector search and MLOps.
  • Evaluate architectural trade-offs across scalability, performance, security, reliability and cost. Platform Engineering & Scale
  • Define engineering standards for high-availability and low-latency AI services.
  • Design scalable architectures using technologies such as Kubernetes, Docker, Apache Kafka, Apache Spark and cloud-native services.
  • Establish patterns for observability, resiliency, fault tolerance, API integration and production AI operations.
  • Guide teams in building reusable platform components instead of isolated point solutions. Technical Leadership
  • Provide architecture leadership across multiple engineering and business teams.
  • Translate business requirements into scalable technical roadmaps and architecture decisions.
  • Lead technical design reviews and help engineering teams resolve complex architectural challenges.
  • Communicate architecture strategy, trade-offs and investment priorities to senior technical and business stakeholders. AI Governance, Security & Responsible AI
  • Establish AI architecture guardrails covering security, privacy, model governance and responsible AI.
  • Partner with cybersecurity, privacy, legal and data-governance teams to embed appropriate controls into AI platforms.
  • Support enterprise compliance requirements, including applicable privacy and AI regulations. AI FinOps & Optimisation
  • Define strategies for efficient use of cloud AI infrastructure, including GPU and accelerator resources.
  • Monitor architectural choices for cost, performance and utilisation.
  • Establish patterns for optimising training, inference and high-compute AI workloads.

Qualifications

Required

  • 8+ years of relevant technology experience, including significant experience designing enterprise-scale distributed or cloud-based platforms.
  • Proven experience architecting and deploying AI/ML and Generative AI solutions in production.
  • Hands-on architectural experience with capabilities such as:Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Vector databases/vector search Model serving and inference Model fine-tuning MLOps and model lifecycle management
  • Strong knowledge of at least one major cloud platform: Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP).
  • Deep understanding of distributed systems, microservices and container orchestration using technologies such as Docker and Kubernetes.
  • Experience with large-scale data and event processing technologies such as Apache Kafka and Apache Spark, or equivalent platforms.
  • Experience designing highly available, fault-tolerant and performance-sensitive production systems.
  • Demonstrated ability to influence architecture and engineering decisions across multiple teams.
  • Strong communication skills with experience engaging senior technical and business stakeholders. Preferred
  • Experience establishing enterprise AI governance, privacy, security and Responsible AI controls.
  • Knowledge of regulatory frameworks relevant to AI and data protection, such as the General Data Protection Regulation (GDPR) and EU Artificial Intelligence Act.
  • Experience optimising GPU or accelerator-based workloads and implementing cloud Financial Operations (FinOps) practices.
  • Experience working within an AI Centre of Excellence (CoE), enterprise platform organisation, or other central technology function.
  • Experience leading enterprise-scale AI adoption or technology transformation programmes.
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