Lead DevOps/ML Engineer

As a Lead MLOps/DevOps Engineer on our Generative AI team, you’ll be at the cutting edge of language model applications, building innovative solutions across key areas of the FICO platform—including fraud detection, decision automation, workflow orchestration, and system optimization and also internal productivity. You will deploy production systems, troubleshoot operational issues, and integrate services at scale. You’ll have the opportunity to make a measurable impact by bringing next-generation AI capabilities into production, collaborating with a world-class team to build robust, scalable infrastructure and accelerate innovation across FICO’s platform.What You’ll ContributeDesign, build, and maintain scalable, resilient data and ML pipelines, infrastructure, and workflows using tools such as Terraform, GitHub Actions, ArgoCD, Helm, and otherAutomate infrastructure provisioning and configuration management using cloud-native services (preferably AWS) with tools like Terraform, CloudFormationDesign, containerize, and manage Kubernetes (EKS) clusters and/or ECS environments in AWS. Collaborate with development teams to optimize performance, deployment, and costPartner with DevOps and SRE teams to ensure high availability, observability, scalability, and security of the data and ML infrastructureWork closely with Data Scientists and ML Engineers to operationalize machine learning models, including building CI/CD pipelines for model training, validation, and deploymentImplement observability for data pipelines and ML services using tools like Prometheus, Grafana, Datadog, or similarDevelop and maintain automated pipelines for model retraining, monitoring drift, and versioning in productionSupport experimentation and prototyping in areas such as Machine Learning and Generative AI, transitioning successful prototypes into production systemsEnsure cloud infrastructure is secure, compliant, and cost-efficient, following best practices in governance, identity, and access managementWhat We’re Seeking8+ years of experience in DataOps, MLOps, or related fields, with 3+ years focused on ML model operationalization and workflow automationionProficient in AWS services including EC2, S3, IAM, ACM, Route 53, CloudWatch, EKS, and ECSExperience with infrastructure as code (IaC) tools such as Terraform, CloudFormation, and HelmFamiliarity with CI/CD for ML pipelines, GitOps practices, and tools like GitHub Actions, Jenkins, or Argo WorkflowsStrong scripting and automation skills using Python, or GitHub workflowsSolid understanding of observability and monitoring tools (e.g., Prometheus, Grafana, Datadog, or OpenTelemetrySolid understanding of security best practices for cloud and Kubernetes environments, including secrets management, identity & access control, and policy enforcementStrong understanding with data governance, lineage, and metadata management is a plusExcellent collaboration and communication skills, with a proven ability to work effectively in cross-functional, globally distributed teamsBachelor’s degree in computer sciences, or a related discipline, or equivalent hands-on industry experience