Job description

Key Responsibilities - SaaS Platform Engineering: - Design, build, and optimize a cloud-native, multi-tenant SaaS platform that scales with Enable’s rapid growth. - Develop and maintain core infrastructure components, including compute, networking, observability, and CI/CD pipelines. - Implement best practices for cloud cost optimization, security, and system reliability. - Enhance API gateways, identity management, and service orchestration for seamless integration across services. Key Responsibilities - MLOps & AI Infrastructure: - Architect and manage scalable AI/ML pipelines for model training, deployment, and monitoring. - Develop and maintain MLOps workflows using tools like Kubeflow and MLflow. - Optimize ML model inference for real-time and batch processing in a production SaaS environment. - Collaborate with data scientists and ML engineers to streamline model lifecycle management. Key Responsibilities - Automation, Observability & Performance: - Implement and refine Infrastructure-as-Code (IaC) practices using Terraform or Pulumi. - Build self-healing, automated monitoring solutions for system health, application performance, and security. - Improve CI/CD processes to support high-velocity engineering teams with minimal operational overhead. - Establish robust logging, tracing, and metrics collection for visibility into SaaS application performance. Key Responsibilities - Cross-Functional Collaboration & Leadership: - Work closely with engineering teams to ensure platform capabilities support product innovation and reliability. - Partner with security teams to ensure compliance with SaaS security and compliance standards and best practices. - Implement monitoring, logging, and alerting solutions to track model and system performance, ensuring compliance with best practices. - Define and document platform engineering best practices to elevate team-wide capabilities. - Mentor junior and mid-level engineers, fostering a culture of technical excellence and continuous learning. Qualifications: - 8+ years of experience in platform, DevOps, or cloud engineering, with at least 3+ years in SaaS environments. - Bachelor’s degree in Computer Science, Engineering, or a related field. - Expertise in architecting, deploying, and managing cloud-native applications on AWS, GCP, or Azure. - Strong experience with Kubernetes, serverless computing, and container orchestration. - Proficiency in modern Infrastructure-as-Code (Terraform, Pulumi) and CI/CD tools (GitHub Actions, ArgoCD). - Experience in distributed systems, service mesh technologies (Istio, Linkerd), and event-driven architectures. - Hands-on experience with databases (SQL, NoSQL, vector DBs), ensuring high availability and performance. - Deep understanding of SaaS security principles, identity management, and compliance frameworks (SOC 2, ISO 27001). - Strong programming and automation skills in Python, SQL, and Bash. - Strong experience with microservices architecture and API development. - Familiarity with MLOps frameworks and ML model operationalization. Preferred Qualifications: - Experience building and maintaining large-scale data processing pipelines. - Expertise in observability tools such as Prometheus, Grafana, OpenTelemetry, or ELK stack. - Familiarity with real-time data processing and messaging platforms (Kafka, Pub/Sub, Kinesis). - Background in high-availability, globally distributed architectures. - Work experience in cutting-edge AI and MLOps challenges at the intersection of ML, engineering, and cloud infrastructure. - Certifications in cloud computing (e.g., AWS Certified DevOps Engineer, Azure DevOps Engineer). - Contributions to open-source DevOps, platform, or MLOps tooling.

Requirements