Lead Cloud Software Engineer

Job Description

• AI is reshaping how the world builds, learns, and connects. At our company, we believe the next generation of enterprise software will be powered by AI-driven platforms that are secure, scalable, and built for constant innovation.
• As our Lead of AI LLM Platform Dev Ops team, you will build solutions that enable this vision.
• From CI/CD pipelines, ML Ops, GenAI, Agentic AI platforms to AI governance and observability, you will define how our engineers build, test and ship the technologies that power global enterprises.
• This is your chance to lead & shape the evolution of Coupa’s AI platform. You will lead system design, code development, deployment, and automation for our LLM/GenAI/Agentic AI platforms ensuring reliability and scalability of infrastructure.
• Lead Platform Architecture and Strategy:
  Define and drive the long-term technical vision for Agentic AI platform and LLM infrastructure, making strategic decisions on technology choices, architectural patterns and platform evolution.
• Design Enterprise-Scale Platform Solutions:
  Architect complex, multi-tenant platform systems that support high availability, scalability, and security requirements across global multi-cloud environments.
• Drive Technical Leadership and Mentorship:
  Lead and mentor a team of platform Dev Ops engineers, providing technical guidance, code reviews, and career development while fostering a culture of engineering excellence.
• Establish Platform Engineering Standards:
  Define and implement best practices, coding standards and architectural principles that ensure consistency, reliability, and maintainability across all platform components.
• Evaluate new products and technologies such as RAG, MCP servers, AI Agents, and Agentic Workflows.
• Deployment and life cycle management of LLM & Embedding Models.
• Build and manage MLOps pipelines.
• Infrastructure as Code (IaC), e.g., Terraform, for multi-cloud deployments.
• Ensure technical quality through code reviews, architecture discussions, and engineering standards.
• Collaborate with the Principal Architect and AI/ML Engineering Lead on technical direction.
• Collaborate with platform, Dev, & QE teams to plan & deploy releases.
• Participate in design reviews, code reviews, troubleshooting incidents and write & review Incident RCAs.
• Manage AWS core & AI/GenAI services (S3, IAM, EKS, Bedrock, etc.) and be an expert in IaC (Terraform).
• Demonstrate architectural decision making:
  Proven ability to make complex technical trade‑offs, evaluate emerging technologies, and drive architectural decisions that balance current needs with long‑term scalability.
• Lead in fast‑moving environments, motivating teams and embracing innovation.
• Experience building enterprise‑grade, scalable, cost‑efficient, secure AI/ML platforms with a focus on resiliency, automation, and observability.
• Strong communication skills across geographies.
• Senior Platform Engineering Leadership: 10+ years of hands‑on platform engineering experience, including 3+ years in technical leadership roles, managing complex cloud infrastructure at enterprise scale.
• Advanced Automation and Programming:
  Expert‑level programming skills in multiple languages (Python, Go, Java, etc.) with extensive experience building sophisticated automation frameworks and platform tools.
• Enterprise Platform Design:
  Proven track record designing and implementing large‑scale platform solutions, including microservices architectures, container orchestration, and distributed systems patterns.
• Advanced Cloud Architecture Expertise:
  Deep expertise in cloud‑native architectures, multi‑cloud strategies, and advanced services across AWS or GCP, with proven experience designing systems that handle millions of transactions.
• BS/MS in Computer Science or equivalent experience.
• Deployment and life cycle management of LLM & Embedding Models (OpenAI, AWS Bedrock, Amazon Sage Maker) and vector databases like LanceDB.
• Strategic CI/CD and Dev Ops:
  Deep expertise in enterprise CI/CD design, MLOps pipelines, and Dev Ops transformation initiatives, with experience implementing these practices across large engineering organizations.
• Enterprise AI platform infrastructure development experience and exposure to evaluation of new products and technologies such as RAG, MCP servers, AI Agents, Agentic Workflows, Lang Chain, etc.
• Containerization and Orchestration:
  Deep knowledge of Docker and Kubernetes (especially GKE or EKS) for scaling inference services, building and deploying workloads.