• Work with the product engineering teams to prototype and experiment with new AI features • Work across backend, frontend, and interact with LLMs and/or other ML models • Productionize, launch, and operate AI-based technology integrations into Cribl’s core products with the goal of solving real customer problems • Stay up-to-date with the latest AI technologies and trends • Work closely with fine tuning models, Prompt Engineering, and Inference Platforms • This position will require stand-by, on-call, or off-hours duties

• Conduct deep-dive assessments of workflows across functions to identify automation and AI augmentation opportunities. • Build and maintain an AI opportunity roadmap, prioritizing use cases by ROI, feasibility, and strategic value, in close partnership with department heads and the CEO. • Stay at the frontier of AI tooling and agent frameworks, continuously evaluating new models, platforms, and techniques to keep Endor Labs ahead of the curve. • Design, build, and deploy AI agent-based workflows and automations that solve real business problems – including multi-step agentic workflows, RAG-based knowledge systems, automated content pipelines, intelligent CRM enrichment, and more. • Own the full lifecycle from rapid prototyping to production-grade deployment, including monitoring, error handling, and iteration based on user feedback. • Integrate AI solutions with existing tools and systems (Salesforce, HubSpot, Slack, Google Workspace, internal databases) to create seamless, end-to-end workflows. • Design and execute a comprehensive change management strategy to drive AI adoption across the organization, from executive alignment to individual contributor enablement. • Develop and deliver training programs, workshops, and hands-on enablement sessions tailored to each department’s specific AI use cases and skill levels. • Identify and cultivate AI champions within each team to accelerate grassroots adoption and create a culture of AI-first thinking. • Track adoption metrics, gather feedback, and iterate on both the tools and the training to ensure sustained, meaningful usage.

• Design and implement NVIDIA NIM connectors and Blueprint integrations into upstream agentic AI frameworks (e.g., LangGraph, LlamaIndex, AutoGen, CrewAI, Claude Agent SDK, OpenAI Agents SDK) • Build and maintain agentic workflows — including tool use, memory, planning, and multi-agent coordination patterns — powered by NVIDIA inference services • Develop test harnesses and evaluation pipelines to validate agent behavior, tool-call correctness, and end-to-end workflow quality • Evangelize NVIDIA agentic AI capabilities through demos, documentation, and upstream community contributions • Partner with NVIDIA NIM and release teams to ensure Day-0 agentic support for new models and inference APIs • Continuously discover emerging agentic frameworks and drive integrations that expand the NVIDIA agentic ecosystem • Mentor junior engineers and review contributions from internal and external contributors

• Own and evolve OpenShell’s CI/CD system across GitHub Actions, self-hosted Linux amd64/arm64 runners, GPU runners, macOS runners, reusable workflows, gated e2e jobs, release canaries, and developer-facing branch checks. • Build and harden multi-architecture release pipelines for GHCR images, Helm OCI charts, Linux and macOS CLI binaries, gateway and sandbox binaries, Python wheels, Debian packages, RPM packages, Homebrew formula generation, and install scripts. • Improve release reliability for both rolling dev builds and tagged public releases, including version derivation, automatic tagging, checksums, artifact pruning, provenance, artifact attestations, and downstream package publishing. • Drive reproducible and performant builds using mise, uv, Cargo, maturin, BuildKit, Docker/Podman, sccache, native amd64/arm64 runners, Zig, osxcross, protobuf codegen, and pinned toolchains. • Own the quality gates that decide whether code is safe to merge or ship, including Rust/Python checks, license headers, markdown/docs validation, e2e label gates, Docker/Podman e2e, Kubernetes/Helm e2e, GPU e2e, and release canary coverage. • Debug difficult build and release failures across containers, registries, runners, package managers, cross-compilation toolchains, kernel/VM runtime artifacts, and CI cache behavior. • Partner with platform engineers to make OpenShell easier to install and operate across Linux, macOS, Kubernetes, Docker, Podman, GPU environments, and experimental VM/libkrun-based runtimes. • Continuously improve CI observability, failure diagnostics, workflow runtime, cache hit rates, artifact traceability, and the developer experience for contributors and maintainers.