Go-to-Market Engineer (AI-first GTM Engineering)

SenseOn is an AI cybersecurity platform helping organisations gain holistic visibility and control of cyber risk. We're building an AI-first go-to-market engine that blends engineering, data, and commercial execution to create pipeline and accelerate customer outcomes.

This is a software-leaning GTM role for someone who can build, ship, and operate. You'll expand and run internal GTM systems (agent workflows, integrations, data pipelines, and internal tools) on a modern cloud stack. You'll work closely with commercial teams to turn GTM needs into reliable, secure, measurable capabilities.

We value people who enjoy end-to-end ownership — defining the problem, shipping the solution, and operating it with high standards.

This role is ideal for an engineer who is excited to apply technical skills to revenue outcomes, communicate clearly with non-technical stakeholders, and develop a strong understanding of cybersecurity.

We're remote-first and welcome applications globally, including from South Africa. To collaborate effectively, this role requires meaningful working-hours overlap with UK time (typically 4+ hours on most weekdays).

• →Build and maintain internal GTM services and automations that support pipeline creation and customer workflows (e.g., routing, enrichment, scoring, orchestration, CRM write-backs).
• →Extend and improve our internal agent stack (Apex) and the workflows that power it.
• →Use Codex, Claude Code, and Gemini to accelerate delivery, while maintaining strong QA and correctness.
• →Build and maintain production-grade integrations via APIs and event-driven patterns (e.g., webhooks).
• →Create and maintain automated build/test/deploy pipelines (CI/CD) so changes ship safely and repeatably (e.g., GitHub Actions or equivalent).
• →Improve reliability and observability (logs/metrics), and debug issues pragmatically when things break.
• →Communicate clearly in writing: what you built, why it works, how to use it, and how to troubleshoot it.
• →Build with a security-first mindset: least privilege, safe secrets handling, auditability, defensive defaults.

• Useful GTM capabilities ship regularly and are adopted because they make work faster and higher quality.
• Changes land through a clean CI/CD pipeline with tests and safe deployment practices.
• Systems are secure, observable, and predictable (clear logs, measurable outcomes).
• Agent outputs are consistently high quality due to good prompting, deterministic code wherever possible, evaluation, guardrails, and QA and learning loops.

• Strong practical coding ability with clear evidence on GitHub (projects you built or meaningful contributions).
• Comfortable working with agentic coding workflows and modern coding assistants (Codex, Claude Code, Gemini) — and validating correctness.
• Experience building software that integrates with other systems: APIs, auth, webhooks, data handling.
• Experience with CI/CD (e.g., GitHub Actions or equivalent): automated tests, build pipelines, and deployment workflows.
• Familiarity with cloud-deployed services: debugging, logs/metrics, reliability basics.
• Clear written communication and good judgement.
• Security-first mindset and curiosity to learn the cybersecurity industry.

• Python (or another backend language) used in production.
• Infrastructure/deployment tooling exposure (even at a basic level).
• Experience building internal tools for commercial teams.
• Exposure to security fundamentals (IAM concepts, threat modelling, secure defaults).

Please include:

• Link to your GitHub profile and 1–3 repos you're proud of.
• A brief note explaining your CI/CD setup on one of those repos (what runs on PR, what runs on merge, how deployments work).
• A short example of how you've used AI coding tools (what you built, and how you tested/validated it).
• Optional: a short technical write-up you've written (README/docs/blog) that explains a system clearly.

• Initial screen: experience, motivation, and quick technical discussion.
• Repo walkthrough: you'll walk us through one GitHub project, including CI/CD and trade-offs.
• Practical exercise: a small build task focused on correctness, security-first thinking, and clear documentation.
• Final conversation: alignment on ownership, communication, and growth.