Technical lead building safe, evaluable LLM agents and AI infrastructure for OpenLoop’s telehealth platform. Setting architecture, observability, retrieval, and model operations direction.
About the role
- GTM AI Engineer responsible for designing and implementing AI-enabled workflows to enhance Vena’s go-to-market efficiency. Collaborating on systems and data analysis within a remote Canadian team.
Responsibilities
- Drive greater efficiency and scalability though the design and implementation of workflows that connect GTM signals (intent, engagement, enrichment, lifecycle events) to outcomes and replace manual, inconsistent processes with standardized, measurable operating patterns. Examples include:
- Signal-driven lead/account qualification + routing (e.g., intent + ICP fit → owner/sequence/next best action), with clear SLAs and exception handling.
- Research + personalization automation that leverages existing systems and pre-built agent concepts (research agents, prospecting agents, sales↔marketing feedback agents, cross-sell agents) to reduce rep/admin time and improve message quality and consistency.
- Partner motion automation tied to partner objects, sourcing taxonomy, and reporting readiness (partner data → dashboards → actions) to improve pipeline attribution and partner execution.
- Build and maintain reliable integrations, automations, and data sync patterns across the GTM stack, improving data quality, reducing tool sprawl, and ensuring actions are triggered from trusted signals.
- Establish an AI/automation operating cadence for GTM: intake → prioritization → build → QA → release → measure → iterate, with clear owners, SLAs, and documentation to reduce ad hoc requests and rework.
- Instrumentation and monitoring for automations/agents (throughput, failure modes, fallbacks, and human-in-the-loop paths) so GTM teams can trust and adopt what’s shipped.
- Define and track success metrics for each workflow (time saved, speed-to-lead, conversion lift, pipeline impact, data quality), and partner with Analytics to make results visible in Power BI.
- Drive adoption through enablement: stakeholder training, playbooks, change management, and feedback loops that turn prototypes into repeatable, scalable workflows used day-to-day.
Requirements
- 5+ years in RevOps / GTM Engineering / Sales Ops Engineering with deep exposure to CRM + MAP + data warehouse environments, and a track record of making GTM teams faster through better systems and automation.
- Strong hands-on ability to build automations/integrations (APIs, webhooks, iPaaS/automation tooling, or lightweight services) with production discipline (testing, monitoring, documentation, and safe rollbacks).
- Advanced SQL and comfort with analytics engineering patterns; ability to define KPI semantics, establish baselines, and ship dashboards that quantify efficiency and pipeline impact.
- Practical experience with at least 2 of: Salesforce, HubSpot, Snowflake, Power BI in production GTM use cases (routing, lifecycle automation, attribution, forecasting/coverage, or pipeline inspection).
- Demonstrated ability to translate ambiguous GTM goals into clear build specs and deliver iteratively (MVP → scale), balancing speed with reliability and stakeholder trust.
- Proven record of partnering cross-functionally (Sales, Marketing, Partners) to drive adoption and change behavior—not just ship tooling.
- Working understanding of GenAI/agent concepts with a bias for governance, QA, and measurability (avoid “demo-ware”), including human-in-the-loop design and prompt/agent iteration.
- Interest in AI and willingness to explore AI-driven solutions to enhance performance and drive efficiencies
- Experience implementing or operationalizing enrichment waterfalls / orchestration and CRM sync patterns.
- Familiarity with agent evaluation/testing methodology and production monitoring patterns for AI features.
- Experience in partner data motions (PRM, co-sell workflows, partner pipeline reporting).
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