• Design and maintain GitHub Actions reusable workflows across a multi-repository ecosystem • Own GitOps deployments through ArgoCD, including promotion workflows, sync policies, drift detection, and automated rollback strategies • Implement deployment safety mechanisms such as environment protections, concurrency rules, and verification gates • Operate and upgrade EKS clusters, including Karpenter provisioning, node groups, and critical cluster add-ons • Maintain Terraform-driven infrastructure and enforce PR-driven workflows through Atlantis • Define and maintain SLOs, SLIs, alerting rules, and monitoring dashboards across platform services • Lead incident response, coordinate recovery efforts, and execute structured post-incident reviews • Participate in an on-call rotation and contribute to improving operational processes • Operate and maintain HashiCorp Vault, including policies, authentication backends, and secret engines • Implement supply-chain security controls, including Trivy scanning, Cosign signing, SBOM generation, and OPA/Gatekeeper enforcement • Partner with Security Engineering on network policies, egress controls, and compliance standards • Automate repetitive tasks and maintain proactive runbooks to reduce operational risk • Use AI tools to improve infrastructure automation, documentation, and deployment safety validation • Collaborate with product teams to strengthen SLOs and deployment safety practices • Challenge technical assumptions and advocate for scalable, secure DevOps architectures

• Design, implement, and operate highly available, scalable services in cloud environments (primarily Azure, with some multi‑cloud scenarios) • Define and evolve SLOs/SLIs, error budgets, and capacity strategies for owned services; use them to guide engineering trade‑offs and release decisions • Analyze patterns in incidents and outages; own long‑term reliability improvements for your domain and contribute to reliability strategy across services • Write high quality code that is easy to maintain and test • Ensure design and architecture is extensible across projects, and participate in technical design and code reviews • Identify operational toil and lead automation efforts to eliminate it—deployment, runbook, and remediation workflows that make incidents rarer and faster to resolve • Develop robust, well‑tested tooling and shared libraries that are adopted across multiple teams • Improve CI/CD pipelines and guardrails to reduce change failure rate while increasing deployment velocity • Design and implement logging, metrics, tracing, and alerting for complex distributed systems; ensure signals are actionable and aligned to business impact • Build and automate tools and solutions for incident impact analysis and effective mitigation • Participate in and often lead incident response for Sev0–Sev2 events: triage, mitigation, coordination, and clear communication • Perform and contribute to blameless post‑incident reviews, root‑cause analysis, and follow‑through on corrective actions • Work with Operations and Incident Command teams during and post incidents to drive excellence in Incident Management Process • Compose and analyze dashboard to highlight areas of the business that need attention and help drive organizational KPI • Create and respond to system generated alerts to maintain system health • Work with Operations and Engineers to fill any gaps in alerting and telemetry • Act as the primary SRE partner for one or more engineering teams—shaping architecture, reviewing designs, and embedding reliability best practices • Mentor and coach other SREs and software engineers on topics such as debugging, observability, incident management, and performance optimization • Contribute to and help standardize SRE practices, runbooks, and production readiness criteria across CPE and product teams • Work with Product Management, collaborators and other developers to understand design requirements and provide estimates for development • Learn and grow in all key technologies in Docusign and be a partner to Eng and Operations teams

• Design, develop, and maintain infrastructure for AI inference workloads, including GPU scheduling, model deployment pipelines, and data access patterns in on-prem environments • Build and manage monitoring and observability tools for AI inference platforms, including dashboards, alerts, and runbooks for model health and system performance • Collaborate with ML engineers and platform teams to design system architecture for AI workloads, integrate inference runtimes, and test performance at scale

• Architect, scale, and maintain self-managed Redis, Kafka, Elasticsearch/OpenSearch, and MongoDB clusters • Collaborate with Product and Engineering teams to design resilient architectures for high-scale, real-time cloud operations • Proactively identify bottlenecks and security risks, implementing robust solutions with minimal supervision • Ensure high availability and disaster recovery for distributed systems through Infrastructure-as-Code and CI/CD best practices • Optimize performance and reliability of cloud-native systems, focusing on observability and automated recovery processes • Mentor team members and contribute to DevOps knowledge-sharing across the organization