In this role, you will act as the backbone of our quantitative analytics and AI engineering teams. Your primary mission will be to design, automate, and operate the end-to-end cloud and AI infrastructure — including CI/CD pipelines, containerized microservices, observability, and platform guardrails — so that our Generative AI applications and modelers can run safely, scalably, and reliably in production.
- Cloud Platform Engineering: Architect and operate highly available, multi-service AI infrastructure on the cloud, managing the full lifecycle of compute, storage, networking, and security for AI workloads.
- AI-Ops / LLMOps Pipelines: Establish robust MLOps and LLMOps pipelines covering the end-to-end lifecycle of Generative AI tools — including model deployment, version control, prompt and artifact tracking, automated evaluation, and continuous monitoring for performance, drift, and hallucination mitigation.
- CI/CD Automation: Design and maintain automated build, test, and deployment pipelines for full-stack AI applications, ensuring seamless and secure continuous integration and delivery across front-end, back-end, and AI components.
- Infrastructure as Code: Manage cloud infrastructure using Infrastructure as Code (e.g., Terraform, Cloud Build, Kubernetes manifests) to deliver reproducible, auditable, and scalable environments.
- Containerization & Orchestration: Build and operate containerized microservices (Docker/Kubernetes), managing scaling, rolling deployments, resource optimization, and service resilience for AI workloads.
- Observability & Reliability: Implement comprehensive monitoring, logging, tracing, alerting, and SRE practices to ensure platform reliability, availability, and performance of AI applications in production.
- Security & Governance: Embed security across the platform — identity and access management, secrets management, network policies, and data protection — ensuring all AI workloads meet Ford's strict data privacy, security, and compliance standards.
- Developer Enablement: Work closely with AI engineers, software engineers, and analytical modelers to provide self-service tooling, environments, and automated workflows that remove friction from development to production.
Skills Knowledge Required- Cloud Platform: 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.
- DevOps / SRE Practices: Strong proficiency in CI/CD tooling, GitOps, containerization (Docker), orchestration (Kubernetes), Infrastructure as Code (Terraform), and cloud-native monitoring and logging.
- AI-Ops / LLMOps: Proficiency in tools and platforms for model deployment, prompt/model versioning, evaluation, tracing, and monitoring of LLM and GenAI outputs.
- Scripting & Automation: Hands-on experience with a programming/scripting language such as Python, or Bash for automating infrastructure and operational tasks, and for building tooling that serves collaborators.
- Software Engineering Fundamentals: 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.
- Security & Compliance: Familiarity with cloud security best practices, identity and access management, secrets management, and compliance standards in regulated environments.
- Analytics Workflow Understanding: Awareness of the typical workflows of data scientists and modelers (data wrangling, feature engineering, model validation) so you can build reliable platforms and pipelines that serve them.
- Qualifications Experience
- Education: Master's or Bachelor's degree in Computer Science, Software Engineering, Cloud Computing, Data Engineering, or a related technical discipline.
- DevOps/Cloud Engineering Experience: 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.
- AI/MLOps Engineering: Proven track record of implementing CI/CD for AI workloads, containerization (Docker/Kubernetes), and cloud infrastructure management (Terraform, Cloud Build, GKE).
- Automation & Reliability: Demonstrated experience with infrastructure automation, incident response, and building observable, self-healing production systems.
- Good to Have (Optional):: Previous exposure to the Banking, Financial Services, or Credit Analytics industries. Experience with Machine Learning engineering and model serving frameworks, or relevant cloud certifications (e.g., Google Cloud Professional DevOps Engineer / Cloud Architect).