GTM AI Solutions Manager, Relex

• Experience with Salesforce platform work (e.g. metadata, data modelling, and CRM data integrity, such as deduplication, enrichment, or pipeline hygiene)
• Working fluency in at least one general-purpose programming language (Python, TypeScript, or similar) and SQL
• Comfort with analytics, such as writing SQL against Salesforce or warehouse data, and building lightweight dashboards to track AI tool adoption and tie usage to pipeline outcomes
• Strong product instincts: able to prioritise across a noisy inbound queue, distinguish signal from noise in field feedback, and make defensible trade-off calls
• Sales-fluent: understands the seller workflow well enough to evaluate field feedback critically, not just record it
• Strong written communication and async operating habits

Desirable

• RELEX’s Sales division is building a portfolio of AI-powered tools, agents, and integrations to amplify the productivity of every seller across the org
• The portfolio is fast-moving: the specific systems in production today will evolve and be joined or replaced by others as vendor capability and field needs shift
• This role is defined by its pattern of work, not by any single system: production ownership of what is deployed, hands-on build execution on what comes next, a tight feedback loop with the field, coordination of the contributor and super user network, and continuous adoption of new vendor capability
• The successful candidate is comfortable with that ambiguity and re-pointing their effort as priorities change
• While anchored in Sales, the AI surface increasingly spans Marketing Operations (ICP definition, lead scoring, signal intelligence) and Customer Success (expansion, retention, customer-side agents)
• The role interfaces with both functions where deployed capability crosses the boundary
• The role partners closely with the Senior Director, GTM AI for Sales, who owns strategy and senior stakeholder engagement
• This role owns the execution layer underneath it and is expected to generate design proposals of their own, particularly around signal-triggered automation and adoption / impact measurement
• Production lifecycle of deployed AI capability:
• Own iteration, bug fixing, regression testing, and version upgrades across all AI capability deployed into the Sales division
• Maintain release readiness as underlying platforms (Claude, Agentforce, Salesforce, MCP-connected systems) ship new versions or features
• Run a prioritised backlog informed by field feedback, vendor changes, and the strategic roadmap
• Hands-on build execution:
• Deliver technical builds against design and direction set by the Senior Director, across whichever stack the current initiative requires
• Expected platforms today include Claude skills authoring, Agentforce configuration, Salesforce platform fluency (e.g. metadata, data model, pipeline integrity, deduplication, enrichment logic, and data hygiene at the layer the AI builds depend on), MCP connectors, and knowledge-graph-style data layers. Expected to remain platform-flexible as the stack evolves
• Write, review, and harden code, configuration, and prompts to a production standard
• Steward the data quality of the CRM and semantic layer that the AI portfolio reads from. Partner with Sales Operations on dedupe, enrichment, and pipeline hygiene work that protects the foundation
• Signal-triggered workflow design:
• Proactively monitor ICP-relevant signals across the Sales surface, such as account events, persona moves, supply chain news, intent data (e.g. 6sense), hiring signals, funding rounds, and customer health changes
• Design agentic workflows that surface or act on those signals, such as proposing new automations, not just reacting to inbound requests
• Partner with the Senior Director on which signals are worth building for, then ship the workflow
• Partner with Marketing Operations on ICP and signal definitions, and partner with Customer Success on customer-side AI surface and handoff workflows
• Field feedback and support loop:
• Operate as the single intake point for bugs, feature requests, and usage questions from the Sales division
• Review and harden AI artefacts contributed by sellers, solution principals, and other internal contributors against quality and security standards before they enter the deployed portfolio
• Triage, fix, escalate, and close the loop with reporters
• Maintain self-serve documentation in Confluence and run lightweight office hours as needed
• Vendor capability adoption:
• Track the Anthropic, Salesforce / Agentforce, and other strategic vendor roadmaps
• When new capability ships, assess impact on the deployed portfolio, regression-test, and surface opportunities to extend or replace existing builds
• Recommend additions or retirements to the portfolio based on capability and field signal
• Adoption, usage and pipeline impact measurement:
• Own usage telemetry across the AI surface — skill invocations, agent actions, workflow completions, contributor activity, and active-user trends across regions and segments
• Attribute AI tool usage to pipeline outcomes (deals influenced, hours reclaimed, conversion uplift, time-in-stage reduction); co-own this analysis with Sales Operations so it complements rather than duplicates their reporting
• Maintain a standing impact narrative for senior stakeholders, refreshed on a regular cadence and surfaced into governance forums