Position OverviewThe AI Ops Engineer, GTM is Agiloft's dedicated AI practitioner for the go-to-market organization, shaping and executing the AI vision across Sales, Marketing, Customer Success, Revenue Operations, and the broader revenue function. Equal parts strategist and builder, this person understands how GTM teams win, where they lose time, and where AI can create meaningful leverage.
Customer and market proximity is a core requirement. The role stays actively involved with customers and the sales process to understand what it takes to sell Agiloft, retain customers, and grow accounts, then uses that insight to experiment, measure outcomes, and scale successful AI-powered GTM patterns.
Reporting to the SVP GTM Intelligence with a dotted line to the VP AI Operations, this role partners with GTM leadership for strategic direction while AI Operations provides architectural standards, governance, tooling access, and organizational alignment. Working with the GTM AI Builders community, the AI Ops Engineer prioritizes and delivers solutions that move the revenue needle while continuously developing AI engineering skills and helping transform how Agiloft goes to market.
Job ResponsibilitiesAI Vision and GTM Strategy- Serve as the primary AI thought leader for GTM, helping Sales, Marketing, Customer Success, and Revenue Operations identify and prioritize the highest-value opportunities to eliminate friction, accelerate revenue, improve customer experience, and increase seller productivity.
- Develop and maintain a GTM-specific AI roadmap aligned with the broader AI Operations strategy, balancing near-term workflow optimization with longer-term transformation and sequencing investments against the highest-impact pain points.
- Maintain active involvement with customers and the sales process to ground AI priorities in market realities and identify moments where AI can change outcomes for both the team and the buyer.
- Contribute the GTM perspective to Agiloft's external AI narrative and thought leadership, and measure initiative impact through adoption, productivity, pipeline influence, customer outcomes, and revenue metrics.
AI Execution and Workflow Optimization- Design, scope, and build AI copilots, agents, retrieval-augmented workflows, and human-in-the-loop automations that augment-not replace-GTM teams across use cases such as pipeline management, outbound sequences, customer health, content generation, meeting intelligence, and contract and renewal triggers.
- Own end-to-end delivery of large-scale GTM AI solutions from concept to production, coordinating with GTM AI Builders, AI Operations, and technical stakeholders.
- Partner with the GTM AI Builders community to advance build maturity through coaching, co-build support, and review of department-originated AI work.
- Monitor deployed tools and automations, iterating based on adoption data, team feedback, and changing business needs; maintain testing, documentation, and handoff standards for continuity and auditability.
- Evaluate GTM-relevant AI tooling and bring recommendations to AI Operations for platform alignment before adoption.
Continuous Experimentation, Development and Alignment- Continuously develop AI engineering skills and stay current on agentic workflows, LLM integrations, automation platforms, and AI-assisted coding and scripting.
- Operate within the AI Builders Program at the L4 AI Ops level for GTM-scoped work, collaborating with L3 Practitioners; register builds in the AI Build Registry and comply with POL-AIOPS-001 and related governance policies.
- Maintain clear communication with the VP AI Operations and GTM leadership on build status, opportunities, blockers, and priorities; coordinate with the Principal Data and Integrations Architect when use cases require data pipelines, Snowflake access, or warehouse connectivity.
- Coach GTM teams on responsible AI adoption and help increase AI fluency across the organization.
- Other duties as assigned.
Required Qualifications- Bachelor's degree in Business, Marketing, Communications, Information Systems, a related field, or equivalent practical experience.
- Experience in a GTM, revenue operations, or customer-facing role at a B2B SaaS company, with exposure to the full revenue cycle from pipeline to renewal.
- Experience with CRM, marketing automation, and CS platforms such as Salesforce, HubSpot, Gainsight, or Outreach as an admin and/or power user or AI workflow integration target.
- 1 to 3 years of direct experience leading AI optimization of GTM workflows, with demonstrated results across sales, marketing, customer success, or revenue operations and examples of initiatives conceived and driven to completion.
- Base-level Python scripting skills sufficient to read, modify, and execute automation scripts, plus the aptitude and commitment to deepen scripting, AI engineering, and architectural fluency through structured learning.
- Foundational knowledge of RAG, embeddings, prompt engineering, agentic workflows, and how LLM-powered tools are built and integrated; technical aptitude to operate as an L4 AI Builder alongside AI Operations.
- Familiarity with AI workflow orchestration platforms such as n8n, Tines, LangChain, or equivalents, including connecting AI tools to GTM systems of record; familiarity with AI-assisted coding tools such as Claude Code or OpenAI Codex.
- Strong customer orientation and experience engaging customers, understanding the buying journey, and using that knowledge to identify AI leverage in the GTM motion.
- Ability to work as a credible strategic partner to senior GTM leaders, translate AI capabilities into business outcomes, and collaborate respectfully across GTM functions and AI Operations.
- Genuine passion for practical AI applications in revenue contexts, with curiosity and self-directed learning that support continuous skill development.
- SaaS industry experience required.
Preferred Qualifications- Familiarity with CLM, legal tech, eDiscovery, or adjacent enterprise software serving legal, procurement, or finance buyers.
- Exposure to enterprise AI governance practices, including policy-based controls, intake processes, and AI build registries, and experience collaborating with a centralized AI or data operations function.
- Familiarity with Snowflake or equivalent cloud data platforms as a consumer of data infrastructure for AI use cases.
- Experience participating in customer events, industry conferences, or field sales activities that deepen understanding of buyer behavior and the competitive landscape.
- Experience in a private equity-backed SaaS organization.
