Senior AI Engineer
Ford Motor Company (company site, opens in a new tab)5+ yrsChennaiHybridDirect career pageEst. 22–40 LPA
Posted on 15 Sept
Reach people at Ford Motor Company
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Ford Motor Company you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, build, and maintain core components of Ford Credit's AI platform including agent frameworks, platform services, deployment pipelines, and knowledge capabilities.
- Embed governance, security, and compliance controls to enable responsible AI development in a regulated financial services environment.
- Collaborate cross-functionally with product, architecture, security, SRE, and business teams to deliver scalable, production-grade AI platform features and support internal customers from concept to production.
Minimum Requirements
- Bachelor's degree in Computer Science, Engineering, Data Science, Machine Learning, or related field (or equivalent experience).
- 5+ years of software engineering experience delivering production-grade applications and services.
- Strong programming skills in Python and/or Java.
- Experience developing cloud-native applications on Google Cloud Platform or comparable cloud environments, including hands-on use of LLMs, agent frameworks, retrieval systems, and tool integration.
Ideal Candidate Profile
- Experienced software engineer comfortable building and operating AI platforms and developer tools in production, especially within regulated environments like financial services.
- Practitioner of engineering best practices including testing, code quality, observability, and maintainability in complex cloud-native systems.
- Collaborates closely with multi-disciplinary teams (product, security, architecture, SRE) to balance innovation with governance, security, and operational excellence.