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Thrive delivers upon the promise of technology by enabling our clients’ digital transformation journeys with best practices on an AI, cybersecurity and cloud focused NextGen managed services platform.
Thrive ’s culture is powered by diverse talent, big ideas, and a customer-first mindset—and it’s what truly sets us apart. Thrive is built for people who want more than a weekday routine. If you’re hungry to grow, eager to tackle new challenges and ready to drive real impact in a rapidly expanding environment, then welcome to THRIVE!
About The Role
We are building AI into the way our go-to-market organization operates, sells, serves customers, and scales. This role exists to identify the highest-value opportunities for AI across the revenue engine, prove the business case with data, and build practical agents and workflows that improve productivity, decision quality, speed, and customer experience.
This is a hands-on, business-facing builder role for someone who can operate across Sales, Marketing, Customer Success, and Revenue Operations. You will not simply receive a backlog. You will create one by finding friction, quantifying the opportunity, designing the solution, building and testing it, launching it with the field, and measuring whether it worked.
The right person blends GTM judgment, AI fluency, data analysis, and enough technical depth to move from idea to production. This is not an advisory-only role and it is not a research role. It is for someone who can ship useful AI-enabled workflows that real teams adopt.
Why This Role Matters Now
AI is moving from experimentation to operating model. For GTM teams, that means the biggest advantage will not come from isolated tools or demos. It will come from thoughtful, governed workflows that help sellers prepare faster, managers coach with better insight, marketers target with more precision, and customer-facing teams act sooner on risk and opportunity.
This role will help define how we responsibly apply AI across the revenue organization, improving scale without simply adding more manual effort or headcount.
Example Use Cases
Sales meeting prep agents that summarize account context, recent activity, open opportunities, risks, and recommended next actions.
Account research workflows that combine internal data and approved external inputs to support prospecting and account planning.
Renewal and churn risk agents that summarize signals, surface next-best actions, and support executive review processes.
CRM hygiene and data quality automation, including enrichment, duplicate detection, field validation, and follow-up prompts.
Post-call and post-meeting workflows that draft follow-ups, update CRM fields, identify action items, and flag coaching opportunities.
Manager insight workflows that summarize pipeline movement, forecast changes, seller activity patterns, and execution risks.
Marketing and sales alignment workflows that help connect campaign engagement to account prioritization and rep action.
What You'll Do
Find the opportunities
Work across marketing, SDR/BDR, sales, customer success, and revenue operations to map how work actually gets done today: the manual steps, the handoffs, the queues, and the places where reps and managers lose time.
Maintain a living, prioritized inventory of AI and automation opportunities across the GTM funnel, scored on impact, effort, risk, and data readiness.
Pressure-test demand: separate problems that need AI from problems that need a process fix, a field on a record, or a better report.
Bring recommendations forward with a clear thesis: what the problem costs today, what the intervention is, what it will cost, and what changes if we do it.
Understand what drives the need Pull and analyze the data behind each opportunity: CRM, marketing automation, engagement and sequencing tools, support and CS platforms, product usage, and finance where relevant.
Quantify the baseline before anything is built: volume, cycle time, conversion, cost per unit of work, error and rework rates.
Define the success metric and the measurement method up front, and instrument the workflow so results are observable rather than anecdotal.
Report on adoption and realized impact after launch, and be candid when something is not working.
Build agents and workflows Design and build AI agents and automated workflows across multiple platforms: CRM-native automation, workflow and integration tools, agent-building platforms, and LLM APIs.
Own the full build cycle: prototype, test against real data, pilot with a small user group, harden, launch, and iterate.
Write and refine prompts, retrieval logic, tool definitions, and guardrails so agents behave reliably in production, not just in a demo.
Handle the plumbing: data flows, integrations, field mapping, deduplication, error handling, and fallbacks to a human when an agent is out of its depth.
Document what you build so it is supportable by someone other than you.
Drive adoption Partner with GTM leaders and frontline teams so what you build gets used: training, enablement materials, office hours, and a feedback loop into the next iteration.
Treat adoption as part of the deliverable. Workflows must be designed, launched, and measured in a way that makes them useful in the field, not just impressive in a demo.
Keep it safe and governed Apply sensible guardrails around data handling, customer-facing output, and human review, particularly anything that touches prospects or customers directly.
Work with IT, security, and legal on tool evaluation, data access, and vendor review.
Track spend across AI tooling and model usage, and keep cost per outcome in view.
What Success Looks Like First 90 days A documented map of GTM processes with the friction points and data gaps called out.
A prioritized opportunity backlog with sizing and a recommended sequence.
At least one automation or agent shipped to production and in daily use.
First year A portfolio of agents and workflows live across more than one GTM function, with measured impact on cycle time, capacity, conversion, or cost.
A repeatable intake-to-launch process others in the org can plug into.
The team's default answer to 'could AI do this?' is grounded in evidence rather than opinion, because you supplied the evidence.
Requirements
5+ years in revenue operations, sales operations, marketing operations, business systems, data analytics, automation, or applied AI, with strong exposure to GTM workflows.
Demonstrated hands-on building: you have shipped AI-enabled workflows, automations, agents, or internal tools that real users adopted, and you can explain what you built, why it mattered, and how you measured impact.
Strong data fluency, including SQL and advanced spreadsheet analysis, with the ability to size an opportunity, define baseline metrics, and build reporting that proves whether the work created value.
Working knowledge of the GTM technology stack, including CRM platforms such as Salesforce or HubSpot, marketing automation, sales engagement, conversation intelligence, customer success, support, and reporting tools.
Experience with LLM APIs, agent-building platforms, prompt design, structured outputs, retrieval or knowledge grounding, tool/function calling, and practical guardrails for production use.
Experience with automation and integration tooling such as Zapier, Make, n8n, Workato, Tray.io, native CRM flows, webhooks, or API-based integrations.
Comfort with light scripting or technical configuration, preferably Python or JavaScript, APIs, JSON, data transformation, and troubleshooting across systems.
Practical understanding of evaluation, testing, monitoring, failure handling, privacy, security, and cost management for AI-enabled workflows.
Strong stakeholder management skills, with the ability to ask good questions, translate between business needs and technical constraints, and challenge requests when the business case is weak.
Self-directed and outcome-oriented. This role is defined by what you discover, build, launch, and improve, not only by what you are assigned.
Nice to Have
Experience in a B2B services, SaaS, MSP, technology, or complex sales environment.
Familiarity with RAG, vector databases, embeddings, re-ranking, evaluation harnesses, or AI observability tools.
Experience with data warehousing, reverse ETL, BI tooling, or analytics engineering.