Technical Systems Engineer (Full-Stack) - Splunk

As a Senior Full Stack Engineer, you will design, build, and ship production software across the full technology stack. You will combine modern web development, AI-assisted engineering, workflow automation, and cloud technologies to deliver scalable, reliable experiences. You will work closely with product teams and stakeholders to translate business requirements into impactful solutions and drive features from architecture through deployment and iteration.

• Own end-to-end delivery of features, including architecture, implementation, testing, deployment, and continuous improvement.

• Build scalable full stack applications across frontend, backend, and data layers, developing clean APIs and intuitive user experiences.

• Design and maintain

to automate internal operations, integrations, and data pipelines, including triggers, branching, error handling, retries, and secrets management.

• Leverage

to accelerate development while maintaining code quality, security, testing, and engineering standards.

• Integrate third-party services, webhooks, and event-driven systems while improving developer velocity through reusable components, CI/CD, observability, and engineering best practices.

• 6+ years of experience building and delivering production full stack applications.

• Strong proficiency in TypeScript/JavaScript and experience with a modern backend framework such as Node.js or Python/FastAPI.

• Hands-on experience with React/Next.js or an equivalent modern frontend framework, along with backend fundamentals including REST/GraphQL, authentication, caching, background jobs, and database design.

• Practical experience building and operating

, including triggers, branching, error handling, retries, and secrets management.

• Experience with AI-assisted engineering workflows and cloud/DevOps practices, including tools such as Cursor or Claude, Docker, CI/CD, environment management, and logging/monitoring.

• Experience integrating LLM APIs such as Claude or OpenAI into products, including RAG, tool use, evaluations, or guardrails.

• Familiarity with MCP, agent orchestration, internal AI tooling, vector databases, embeddings, or AI product observability.

• Experience with workflow platforms such as Zapier, Make, Temporal, or Airflow.

• Experience working with machine data, observability, or data-intensive platforms.

• Strong security-focused development practices, including authorization, secrets management, input validation, and secure API design.