AI Ops Engineer

Overview

Build, automate, and operate the cloud and AI infrastructure that runs Ford Credit's Generative AI applications, so that AI engineers and modelers can run them safely, scalably, and reliably in production.

What you'll do

• Architect and operate highly available, multi-service AI infrastructure on the cloud, managing compute, storage, networking, and security for AI workloads.
• Establish MLOps and LLMOps pipelines covering model deployment, version control, prompt and artifact tracking, automated evaluation, and continuous monitoring for performance, drift, and hallucination mitigation.
• Design and maintain automated build, test, and deployment pipelines for full-stack AI applications across front-end, back-end, and AI components.
• Manage cloud infrastructure using Infrastructure as Code (e.g., Terraform, Cloud Build, Kubernetes manifests) to deliver reproducible, auditable, and scalable environments.
• Build and operate containerized microservices (Docker/Kubernetes), managing scaling, rolling deployments, resource optimization, and service resilience for AI workloads.
• Implement monitoring, logging, tracing, alerting, and SRE practices to ensure platform reliability, availability, and performance of AI applications in production.
• Embed security across the platform, including identity and access management, secrets management, network policies, and data protection, to meet Ford's data privacy, security, and compliance standards.
• Work closely with AI engineers, software engineers, and analytical modelers to provide self-service tooling, environments, and automated workflows.

What you'll need

• Master's or Bachelor's degree in Computer Science, Software Engineering, Cloud Computing, Data Engineering, or a related technical discipline.
• 3–5 years of overall experience in cloud engineering, DevOps, or SRE, with at least 1–2 years of dedicated, hands-on experience deploying and operating AI, ML, and Generative AI applications in production.
• Extensive hands-on experience with a leading cloud provider (e.g., Google Cloud Platform), including Cloud Run, Cloud Build, GKE, GCS, BigQuery, IAM, VPC networking, and Secret Manager.
• Strong proficiency in CI/CD tooling, GitOps, containerization (Docker), orchestration (Kubernetes), Infrastructure as Code (Terraform), and cloud-native monitoring and logging.
• Proficiency in tools and platforms for model deployment, prompt/model versioning, evaluation, tracing, and monitoring of LLM and GenAI outputs.
• Hands-on experience with a programming or scripting language such as Python, or Bash, for automating infrastructure and operational tasks and building tooling.
• Working knowledge of full-stack application architecture and modern deployment patterns for AI tools, with the ability to integrate front-end, back-end, and AI services reliably.
• Familiarity with cloud security best practices, identity and access management, secrets management, and compliance standards in regulated environments.
• Awareness of the typical workflows of data scientists and modelers (data wrangling, feature engineering, model validation).
• Proven track record of implementing CI/CD for AI workloads, containerization (Docker/Kubernetes), and cloud infrastructure management (Terraform, Cloud Build, GKE).
• Demonstrated experience with infrastructure automation, incident response, and building observable, self-healing production systems.

Nice to have

• Previous exposure to the Banking, Financial Services, or Credit Analytics industries.
• Experience with Machine Learning engineering and model serving frameworks.
• Relevant cloud certifications (e.g., Google Cloud Professional DevOps Engineer / Cloud Architect).

Details

• Location: GBS Chennai.