Onemagnify
From Onemagnify's careers page
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In this role, you will act as the backbone of our quantitative analytics and AI engineering teams.
The Ford Credit Global Data Insights and Analytics (FC - GDIA) department is looking for a motivated and talented AI – Ops Engineer with strong cloud infrastructure and AI operations expertise to build, automate, and secure the platform that powers our advanced artificial intelligence solutions for the Ford Credit team. 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.
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.
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).
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