Gorilla Logic

DevOps Engineer

• Manage, operate, and troubleshoot production Kubernetes environments and workloads
• Build, maintain, and improve infrastructure using Terraform and Infrastructure as Code practices
• Manage application deployments, upgrades, configuration changes, and complex deployment lifecycles
• Work with GitOps-based deployment processes and tools such as Argo CD
• Develop and maintain Python scripts and tooling for platform automation and operational workflows
• Troubleshoot and resolve production incidents across infrastructure, applications, and platform services
• Improve platform reliability, scalability, observability, and operational efficiency
• Support Kubernetes scaling and autoscaling strategies for production workloads
• Collaborate with development and platform teams to resolve infrastructure and deployment challenges
• Participate in technical decisions and identify risks, reliability concerns, and improvement opportunities
• Maintain high engineering and quality standards, providing technical pushback when necessary
• Take ownership of platform initiatives and drive issues through resolution with minimal supervision

Senior Platform DevOps Engineer

Security Engineer

• Own day-to-day security operations
• Support the company's compliance programs
• Serve as the company's security point of contact in a sole-coverage role
• Work hand-in-hand with the small DevOps team, with DevOps support for execution
• Perform hands-on cloud security engineering across AWS-first infrastructure
• Triage security alerts and respond to cloud incidents
• Implement Terraform changes through pull requests
• Apply Kubernetes security practices, including RBAC and workload hardening
• Manage vulnerability scanning, prioritization, and remediation coordination
• Support SOC 2 audits by providing control evidence and working with auditors
• Address CI/CD and software supply-chain security

Senior Cloud Security Engineer

Full-stack Engineer

• Design and ship user-facing features backed by LLMs
• Implement evaluation, failure handling, and cost management for LLM features
• Make architectural decisions and defend tradeoffs across speed, cost, complexity, and risk
• Build highly available systems with sensible latency budgets, graceful degradation, and no single points of failure
• Handle PII and financial data responsibly
• Build automation and data pipelines supporting product features
• Own deployment and operation of services
• Help set the team's direction on AI tooling and approach
• Define requirements in a greenfield, fast-moving environment
• Own shipped work from prototype through production

Senior AI Fullstack Engineer

AI Engineer

• Design and build production-grade services, APIs, SDKs, internal tools, and reusable platform capabilities
• Develop systems combining AI agents, tools, deterministic workflows, APIs, events, and human approval steps
• Implement agent runtimes, model invocation, reusable skills, backend services, and automated actions
• Build orchestration patterns involving routing, sequencing, parallel execution, delegation, retries, checkpoints, scheduling, memory, and state management
• Integrate enterprise context through MCP, RAG, repositories, tickets, documents, databases, search systems, and APIs
• Connect the Harness with GitHub, Azure DevOps, Jira, Slack, cloud services, SaaS products, and internal applications
• Implement identity, permissions, policies, approval gates, guardrails, access controls, audit trails, and operational kill switches
• Build observability for agent activity, distributed traces, logs, metrics, token consumption, model and tool calls, failures, latency, and cost
• Create automated evaluations, benchmark datasets, regression suites, quality thresholds, hallucination checks, and model or prompt comparisons
• Improve developer experience through APIs, SDKs, CLIs, templates, configuration systems, documentation, and reusable reference implementations
• Build lightweight user interfaces, administrative tools, approval experiences, or operational dashboards when required
• Deploy and operate solutions across AWS, Azure, or Google Cloud using containers, serverless services, CI/CD, infrastructure as code, IAM, networking, and secrets management
• Conduct technical discovery in unfamiliar client environments and translate business and technical needs into scalable implementation patterns
• Move from discovery and experimentation to working integrations and production-ready solutions
• Communicate architecture decisions, risks, and trade-offs to client engineers, architects, product teams, and technical leadership

Senior Platform DevOps Engineer

Senior Cloud Security Engineer

Senior AI Fullstack Engineer

Senior Forward Deployed Engineer, AI Platform