Tech Lead Platform Engineer - AI & ML Ops

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.