AI GTM Engineer

ScaleOps, the leader in real-time automated cloud resource management, is revolutionizing how DevOps teams manage their cloud-native application infrastructures. Backed by venture capital and software industry titans, ScaleOps’ platform removes the organizational friction between application owners and DevOps teams by fully automating the resource management process to meet real-time demand.

The ScaleOps platform dynamically manages the application’s resource allocation, eliminating the need for manual intervention. The result is improved application performance, 60%- 80% cloud cost savings, and a fully automated allocation process.

With well over $80 million in backing, ScaleOps has seen tremendous business growth, attracting global industry leaders to its customer base. ScaleOps automatically manages the production environments of over 50 enterprises, including Wiz, CATO Networks, Outreach, SentinelOne, Maxar, Playtika, Orca Security, EQ Bank, Outbrain, PayU, and Noname.

Here’s what you’ll be doing

The GTM AI Engineer sits in the Business Operations department and owns the identification, prioritization, and delivery of AI-driven workflow improvements across the company. This role translates business needs into scalable AI solutions, prompt libraries, agents, and no/low-code automations, embedded directly into core business systems (e.g., CRM, support, knowledge base, etc.). This is not a software engineering role, it sits within Business Operations & Business Applications and partners closely with IT/Security and R&D as needed.

Key Responsibilities

• Build production-grade AI workflows that streamline GTM execution (e.g., lead/account research, enrichment, routing, follow-ups, CRM hygiene, customer handoffs).
• Develop and ship intelligent automations using orchestration tools and LLM frameworks, selecting the right approach for reliability, scale, and maintainability.
• Create secure connectors between AI agents and business systems/data, including extending existing connector patterns when needed to enable safe, permissioned access.
• Take ownership of deployed agents and automations: monitor performance, improve precision/recall, reduce failure modes, and increase measurable business impact over time.
• Lead initiatives independently: define scope, translate requirements into designs, build, test, roll out, and iterate based on observed usage and results.
• Partner with stakeholders across Sales, Marketing, RevOps, CS, and Support to map processes, identify bottlenecks, and prioritize high-ROI opportunities.
• Drive adoption through enablement: training, documentation, office hours, internal champions, and continuous feedback loops.
• Collaborate with Business Applications and Data peers to ensure solutions are aligned with system architecture, data definitions, and operational standards.
• Stay current on evolving AI capabilities, run lightweight experiments, and convert promising ideas into practical internal products.