Note: The job is a remote job and is open to candidates in USA. Coupa makes margins multiply through its community-generated AI and industry-leading total spend management platform for businesses large and small. The Lead Data Platform Engineer will manage data pipelines and AWS services, lead operational lifecycles for ML and GenAI infrastructure, and collaborate with product development teams to create AI-driven features.
Responsibilities
- Manage end-to-end Data pipeline (ETL jobs) within agreed SLAs
- Manage AWS core and big data services (S3, IAM, EMR, Redshift, etc..)
- Running applications in containers (ECS, Docker)
- Lead Day 2 operational lifecycle for ML and GenAI infrastructure. This includes designing, deploying, and maintaining high-availability production LLM serving platforms, implementing automated scaling, self-healing, and infrastructure-as-code patterns. Focus on proactive reliability, model performance observability, and continuous cost optimization for high-compute AI workloads
- Collaborate closely with our product development and engineering teams to create AI-driven features
- Drive cloud operations consistency by automating platform maintenance, standardizing infrastructure configurations (IaC), and implementing robust release management processes to minimize drift across multi-cloud environments
- Manage AWS infrastructure using code (Terraform, Chef, etc..)
- Administering applications running in Linux operating system
- Enable application and system monitoring for better observability
- Application and infrastructure support for ETL jobs and data pipelines including participating in an on-call rotation for after-hours emergencies
- Collaborate with platform and Dev teams to plan and deploy product releases and patch Linux/ECS clusters
- Ability to participate in design reviews, code reviews, and troubleshooting incidents
- Ability to operate in a high-pressure environment and troubleshoot complex issues quickly while successfully handling multiple priorities
- Ability to record, write, and review RCAs
Skills
- Bachelor's Degree and at least 8+ years of experience managing Big Data technologies and Data Pipelines
- Sound knowledge and experience in Linux administration and troubleshooting
- 5+ years of experience in managing cloud infrastructure and platforms, such as AWS and Azure
- Familiar with the current engineering landscape in the generative AI space and have a strong interest in AI and related technologies
- Strong expertise in MLOps and production-grade LLM operations. Proven track record in managing high-availability model inference clusters, automating model lifecycle management, and implementing advanced observability (latency, throughput, and error rate monitoring) specifically for AI workloads
- Have Bash or Python scripting experience
- Experience with containerization, Amazon ECS, EKS/ Azure AKS
- Experience with tools like Chef, Ansible, Jenkins, Rundeck, or equivalent
- Experience with source control systems such as Git and operating in complex branching strategies
- Experience with Infrastructure as Code products like Terraform, helm charts
- Good understanding of DNS and Load balancers setup and troubleshooting
- Experience in Big Data platforms/Data lakes and managing Business Intelligence tools (like looker..)
- Knowledge in ApacheSpark architecture and troubleshooting Java applications
- Basic understanding of MySQL Server and general database knowledge
- Excellent written and verbal communication with a passion for solving the problem
- Confidence in your ability to own and deliver projects and issues to resolution on your own & can think and act globally
- Deep experience in Day 2 cloud operations, including automated incident remediation, capacity planning, and managing large-scale production cloud environments with a focus on performance and reliability
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