Senior Software Engineer, Internal Tools & Automation

Senior Software Engineer, Internal Tools & Automation Tatari is on a mission to revolutionize TV advertising. Founded in 2016 to help transform the antiquated world of TV advertising through the intelligent application of AI and machine learning, Tatari helps some of the world’s fastest growing brands including Chime, Calm, Tecovas, Manscaped, Saatva, and Liquid I.V., reach their customers using linear and streaming TV ads. Our platform combines sophisticated media buying with proprietary analytics to turn TV advertising into an automated, digital-like experience, enabling businesses of any size to advertise on TV. We are looking for a Senior or Staff engineer to join engineering at Tatari, working closely with the SRE team, and lead how Tatari's engineers build software. Every hour an engineer spends fighting a slow CI pipeline, a flaky test, a confusing deploy, or a manual process is an hour not spent shipping product. The arrival of AI coding tools has made the gap between a great developer experience and a mediocre one wider than ever. You will own that gap. You will drive the strategy and hands‑on execution for developer experience and AI tooling across our engineering organization: evaluating and rolling out AI coding assistants, building internal LLM‑powered agents and automations, bringing episodic developer environments to our microservice landscape, speeding up the edit-build-test-deploy loop, and measuring the impact of all of it. You'll work from a foundation of solid infrastructure engineering, and occasionally contribute to core reliability and infrastructure work, but the majority of your time is spent making Tatari's engineers (human and AI alike) faster and happier. Working with a small team, this job affords a lot of agency and the opportunity to work with users across application engineering, data science, and analytics. Responsibilities Own the strategy, roadmap, and execution for developer experience and AI tooling across Tatari engineering Evaluate, deploy, and drive adoption of AI coding assistants and agentic development tools across the engineering org including enablement, best practices, guardrails, and cost management Design and build internal AI‑powered agents and automations for engineering workflows: CI failure triage, incident response tooling, code review assistance, operational runbooks, and internal bots Identify and eliminate friction in the development inner loop: CI/CD pipeline speed and reliability, build and test infrastructure, local development environments, and deployment workflows Design and implement episodic developer environments across our microservice landscape: on‑demand, production‑like environments for developing, testing, and previewing changes Define and instrument developer productivity metrics (DORA‑style delivery metrics, AI tooling adoption and impact) and use them to prioritize where the leverage actually is Partner with application engineers, data scientists, and analytics teams by sitting with them while they work, learning their current process firsthand, and turning what you observe into improvements they will actually adopt Participate in technical architecture discussions Qualifications 5+ years of experience in cloud‑based infrastructure, platform, or developer tooling roles (AWS preferred); for Staff level, a track record of driving tooling or platform adoption across multiple teams Daily, hands‑on use of AI coding assistants (Claude Code, Cursor, Copilot, or similar) in your own workflow, with informed opinions about where they create leverage and where they don't Experience building with LLM APIs and agentic workflows (function/tool calling, MCP, prompt design, or agent frameworks) Experience owning or significantly improving CI/CD systems, build/test infrastructure, or developer environments, with measurable results Strong Python and shell scripting skills Experience with containerization and orchestration (Kubernetes, Docker, or similar) Experience with infrastructure‑as‑code (Terraform preferred) Empathy for the frustrations and toil that slow engineering teams down and an intrinsic drive to fix them Nice to have Experience running an AI tooling rollout or enablement program at a previous company Experience with LLM evaluation approaches for internal tooling (measuring quality, not just adoption) Experience building episodic or preview environments (per‑PR deployments, namespace‑based isolation, sandboxes) in a microservice architecture Go or Rust Experience with GitHub Actions at scale, build caching, or test parallelization/flake management Experience managing RDBMS (Postgres/RDS, Redshift, #J-18808-Ljbffr Tatari