Sr. System Engineer — Workforce AI Company: Cornerstone OnDemand India Team: Workforce AI Location: Hyderabad, India Employment type: Full-time (permanent) Level: Sr. System Engineer (mapped to experience) Experience: 3–5 years of relevant experience Reports to: [Engineering Manager, Workforce AI] Shift: General shift, with periodic on-call rotation aligned to US time zones About the role We are hiring a System Engineer (level: System Engineer or Sr. System Engineer based on experience) to join Cornerstone's Workforce AI team in Hyderabad. You will work on platform-level feature development, data integrations, and end-to-end observability — building the cloud platform and frameworks that AI product teams across Cornerstone depend on every day. You will write real code in Java, Python, and Node.js, ship production infrastructure on AWS (EKS/ECS), and own the developer experience for the systems you build. This is a builder role on a small, high-trust platform team. You will ship production code and infrastructure, debug production issues with rigor, and be the kind of teammate others reach out to first. In this role you will... Drive platform-level feature development for Workforce AI — design and ship shared services, frameworks, and SDKs that AI product teams build on top of. Build and operate data integrations — pipelines, connectors, and event flows that move data between Workforce AI services and upstream/downstream systems (Cornerstone product data, customer tenants, AI/ML stores). Implement end-to-end observability — ship logs, metrics, and traces into ELK and Splunk; build dashboards, SLOs, and alert rules that catch real problems before customers do. Stand up, scale, and harden containerized workloads on Amazon EKS and ECS, including networking, autoscaling, secrets, and zero-downtime deploys. Implement CI/CD with Jenkins (and equivalents), enabling fast, safe delivery from commit to production across multiple services. Design and tune persistence layers on native databases (RDS / Aurora, DynamoDB, PostgreSQL, MySQL) — schema design, indexing, query and cost optimization. Write production code in Java, Python, and Node.js — clean, tested, well-documented modules that other engineers can build on. Build and maintain infrastructure-as-code in CloudFormation, Terraform, and Helm charts — repeatable, peer-reviewed, environment-parameterized. Partner with security, SRE, and Workforce AI product teams to set platform standards, document runbooks, and continuously improve developer experience. Triage and resolve incidents with rigor — reproduce, isolate, fix, and write blameless postmortems to prevent recurrence. You’ve got what it takes if you have… 3–5 years of relevant software / platform / infrastructure engineering experience. Hands-on AWS production experience — EKS and/or ECS, IAM, VPC, networking, S3, EC2, Lambda, RDS/DynamoDB. Proficiency in at least two of: Java, Python, Node.js — comfortable contributing real code, not just glue scripts. Strong CI/CD experience with Jenkins (or equivalents: GitHub Actions, GitLab CI, CircleCI). Working knowledge of Infrastructure-as-Code — CloudFormation, Terraform, and Helm charts in a production repo. Operational experience with ELK and/or Splunk — building dashboards, writing alert rules, debugging from logs. Solid grasp of relational and NoSQL databases (PostgreSQL/MySQL/Aurora, DynamoDB) — schema, indexes, query plans. Strong fundamentals: Linux, networking, containers (Docker), Git, code-review etiquette. Excellent problem-solving and communication — clear writing, calm under incident, generous with help to teammates. Nice to have AWS certifications (Solutions Architect Associate / DevOps Engineer Associate). Exposure to AI/ML platform components — model deployment, vector databases, GenAI infrastructure on AWS (Bedrock, SageMaker). Experience operating Kafka, RabbitMQ, or similar streaming/messaging systems. Service-mesh (Istio/Linkerd), API gateway, or platform-engineering exposure (Backstage, internal developer platforms). Cost FinOps awareness — tagging strategies, rightsizing, savings plans. Security-conscious shipping — SAST/DAST, secrets management (Vault, AWS Secrets Manager), threat modeling. #LI-OnSite