Lead ML Ops/DevOps Engineer – AI Engineering

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FICO

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1956

💸 Finance

🤖 Artificial Intelligence

☁️ SaaS

Finance • Artificial Intelligence • SaaS

FICO is a leading analytics and software company renowned for its FICO® Score, a tool widely used by lenders to assess credit risk. The company offers a comprehensive platform that leverages data, AI, and machine learning to power intelligent decision-making and customer engagement across various industries. FICO's solutions span fraud detection, credit scoring, and customer lifecycle management, making it vital to sectors such as finance and telecommunications. Its innovative products help businesses optimize outcomes through real-time analytics, business composability, and scenario management.

📋 Description

• Design, build, and maintain scalable, resilient data and ML pipelines, infrastructure, and workflows using tools such as Terraform, GitHub Actions, ArgoCD, Helm, and others. • Automate infrastructure provisioning and configuration management using cloud-native services (preferably AWS) with tools like Terraform, CloudFormation. • Design, containerize, and manage Kubernetes (EKS) clusters and/or ECS environments in AWS. • Collaborate with development teams to optimize performance, deployment, and cost. • Partner with DevOps and SRE teams to ensure high availability, observability, scalability, and security of the data and ML infrastructure. • Work closely with Data Scientists and ML Engineers to operationalize machine learning models, including building CI/CD pipelines for model training, validation, and deployment. • Implement observability for data pipelines and ML services using tools like Prometheus, Grafana, Datadog, or similar. • Develop and maintain automated pipelines for model retraining, monitoring drift, and versioning in production. • Support experimentation and prototyping in areas such as Machine Learning and Generative AI, transitioning successful prototypes into production systems. • Ensure cloud infrastructure is secure, compliant, and cost-efficient, following best practices in governance, identity, and access management.

🎯 Requirements

• 8+ years of experience in DataOps, MLOps, or related fields, with 3+ years focused on ML model operationalization and workflow automation. • Proficient in AWS services including EC2, S3, IAM, ACM, Route 53, CloudWatch, EKS, and ECS. • Experience with infrastructure as code (IaC) tools such as Terraform, CloudFormation, and Helm. • Familiarity with CI/CD for ML pipelines, GitOps practices, and tools like GitHub Actions, Jenkins, or Argo Workflows. • Strong scripting and automation skills using Python, or GitHub workflows. • Solid understanding of observability and monitoring tools (e.g., Prometheus, Grafana, Datadog, or OpenTelemetry). • Solid understanding of security best practices for cloud and Kubernetes environments, including secrets management, identity & access control, and policy enforcement. • Strong understanding with data governance, lineage, and metadata management is a plus. • Excellent collaboration and communication skills, with a proven ability to work effectively in cross-functional, globally distributed teams. • A bachelor’s degree in computer sciences, or a related discipline, or equivalent hands-on industry experience.

🏖️ Benefits

• An inclusive culture strongly reflecting our core values: Act Like an Owner, Delight Our Customers and Earn the Respect of Others. • The opportunity to make an impact and develop professionally by leveraging your unique strengths and participating in valuable learning experiences. • Highly competitive compensation, benefits and rewards programs that encourage you to bring your best every day and be recognized for doing so. • An engaging, people-first work environment offering work/life balance, employee resource groups, and social events to promote interaction and camaraderie.