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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Deployment Engineer - **Company:** Resolve AI, Inc. - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Continuous Integration, Cursor, DevOps, Github, Python (Programming Language), Software Maintenance, Role-Based Access Control, Prometheus, Security Assertion Markup Language (SAML), Systems Integration, TypeScript, Datadog, Data Processing, Lightspeed, Large Language Models, Claude Code, Grafana, Agentic-AI, Kubernetes, Evaluation of Large Language Models, DeepMind, Splunk, Vmware - **Published:** October 8, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=f88f635276939822 ## About the Role * 5+ years in a technical, customer-facing role such as deployment or implementation engineering, forward-deployed or post-sales engineering, solutions architecture, or technical consulting. Alternatively, you're an SRE, DevOps, or platform engineer who wants to work directly with customers. * Deep hands-on experience with production infrastructure: cloud platforms (especially AWS), Kubernetes, CI/CD, and observability tools (e.g., Datadog, Grafana, Prometheus, Splunk, OpenTelemetry). You've been on-call and know how incidents actually get resolved. * Active power user of AI agents and coding tools (e.g., Claude Code, Codex, Cursor) who has built them into your own workflows. Comfortable with prompting, context engineering, and critically evaluating LLM output. * A track record of taking enterprise customers from kickoff to production and measurable adoption, across multiple stakeholders and competing priorities. * Working knowledge of enterprise security and access: SSO/SAML, RBAC, data handling, and security reviews. * Able to script and build in Python, TypeScript, or Go for integrations, automations, and prototypes. * Experience running technical workshops and enablement for engineering teams, and explaining complex concepts clearly to both engineers and leadership. * A strong sense of ownership. You raise risks early, you don't let a ticket stand in for owning the customer outcome, and you thrive in the ambiguity of an early-stage company. Nice to have * Experience building or using LLM evaluation frameworks. * Published technical guides, blog posts, or open-source contributions. * Experience with consumption- or usage-based products. ## Description * Software maintenance and production troubleshooting have become a massive tax of engineering velocity. Resolve AI is solving this by building a transformative, truly autonomous AI Production Engineer that investigates and fixes complex system issues end-to-end. * Our founders (Spiros Xanthos and Mayank Agarwal) are the core creators of OpenTelemetry and led Splunk Observability. They have had 2 successful exits to Splunk and VMware. * We've raised over $190M from top-tier investors including Lightspeed, Greylock, DST, Unusual Ventures, and individual backers such as Jeff Dean (Chief Scientist, Google DeepMind), Thomas Dohmke (CEO, GitHub), Matt Garman (CEO, AWS), Reid Hoffman (Founder, LinkedIn), and Fei-Fei Li (Professor, Stanford). ABOUT RESOLVE AI * Software maintenance and production troubleshooting have become a massive tax of engineering velocity. Resolve AI is solving this by building a transformative, truly autonomous AI Production Engineer that investigates and fixes complex system issues end-to-end. * Our founders (Spiros Xanthos and Mayank Agarwal) are the core creators of OpenTelemetry and led Splunk Observability. They have had 2 successful exits to Splunk and VMware. * We've raised over $190M from top-tier investors including Lightspeed, Greylock, DST, Unusual Ventures, and individual backers such as Jeff Dean (Chief Scientist, Google DeepMind), Thomas Dohmke (CEO, GitHub), Matt Garman (CEO, AWS), Reid Hoffman (Founder, LinkedIn), and Fei-Fei Li (Professor, Stanford). Joining Resolve AI at this stage of our journey is a once-in-a-lifetime opportunity. You've already decided that you want to work at an AI-native company that's pushing the limits of how engineers work, and now you're looking for the right one., Our customers run Resolve AI against their most critical production systems. As a Deployment Engineer, you turn a technical win into production results: you implement Resolve in the customer's environment, tune it until their engineers trust its investigations, and drive the adoption and sustained usage that proves its value., * Own implementation end-to-end for your assigned accounts. Confirm scope and success criteria with the Solutions Engineer, build a mutual implementation plan and live blocker log with the customer, and drive to a production-ready deployment. * Deploy and configure Resolve in the customer's environment. Size the deployment footprint, connect and validate each integration (observability, logs, metrics, code, ticketing, chat) one at a time, and configure SSO, permissions, and data-redaction controls against the customer's security requirements. * Build the knowledge that makes investigations accurate. Model the customer's teams and on-call structure in Resolve, co-author runbooks, dashboard guidance, and context files with customer SMEs, and configure alert filters and auto-investigation rules for the right coverage and cost. * Prove quality before scale. Run back-tests against historical incidents, build customer-specific evaluation sets, review results with customer SMEs, and close gaps until they explicitly sign off that Resolve is ready for real incidents. * Drive activation and adoption. Turn on production workflows, lead hands-on enablement and office hours, and embed Resolve in the customer's incident SOPs and runbooks so it becomes the default first responder. * Own sustained consumption. Monitor active users, investigations, credit usage against plan, adoption breadth, and quality. Run reactivation plays when usage dips, and keep configuration and knowledge current as the customer's environment changes. * Be the voice of the field to Product and Engineering. Separate account-level issues from genuine product or model limitations, route gaps and defects with clear customer impact, and oversee fixes through to verification in the customer's workflow. * Operate as one account team with Sales. Share weekly progress and risks with your Account Executive and Solutions Engineer, supply the usage, quality, and ROI evidence behind monthly updates and QBRs, and surface expansion opportunities. * Build leverage for the team. Create reusable implementation recipes, knowledge patterns, playbooks, and agent-driven automations so every deployment is faster and better than the last.