AI Solutions Engineer3

About this role

The Company
Serving the People Who Serve the People
Granicus is driven by the excitement of building, implementing, and maintaining technology that is transforming the Govtech industry by bringing governments and its constituents together. We are on a mission to support our customers with meeting the needs of their communities and implementing our technology in ways that are equitable and inclusive. Granicus has consistently appeared on the GovTech 100 list over the past 5 years and has been recognized as the best companies to work on BuiltIn.
Over the last 25 years, we have served 5,500 federal, state, and local government agencies and more than 300 million citizen subscribers power an unmatched Subscriber Network that use our digital solutions to make the world a better place. With comprehensive cloud-based solutions for communications, government website design, meeting and agenda management software, records management, and digital services, Granicus empowers stronger relationships between government and residents across the U.S., U.K., Australia, New Zealand, and Canada. By simplifying interactions with residents, while disseminating critical information, Granicus brings governments closer to the people they serve—driving meaningful change for communities around the globe.
Want to know more? See more of what we do here.
Job Summary
The AI Solutions Engineer designs, builds, evaluates, and operates AI-powered systems that support revenue workflows across Sales, Marketing, Customer Success, Support, and GTM Operations. This role is responsible for turning AI designs and requirements into production-grade, measurable, and reliable AI solutions that deliver demonstrable business impact.
This role sits at the intersection of AI engineering, Revenue Operations, and systems delivery. The RevOps AI Engineer partners closely with RevOps AI Product Management, AI Solutions Architects, and GTM stakeholders to implement AI agents, workflows, and automation that are grounded in trusted data, governed content pipelines, rigorous evaluation frameworks, and continuous post-launch measurement.
This role is ideal for an engineer who values reliability, quality, and outcomes over experimentation, and who can operate AI systems as durable operational capabilities embedded in core revenue workflows.
What Your Impact Will Look Like
Essential Function
AI Engineering & Implementation

• Build, test, deploy, and operate AI-powered workflows, agents, and automations embedded within core RevOps systems (e.g., Salesforce, Microsoft 365, Azure AI & Copilot surfaces).

• Implement prompt logic, orchestration flows, tool calling, and context-retrieval mechanisms for LLM-based systems.

• Partner with AI Solutions Architects to translate approved solution designs into scalable, maintainable, and secure implementations.

• Ensure AI solutions are production-ready, observable, resilient, and aligned to defined business outcomes.

• Support iterative enhancement cycles based on measured performance and stakeholder feedback.

LLM Evaluation & Quality Frameworks

• Design, implement, and maintain LLM evaluation frameworks that assess accuracy, relevance, consistency, and outcome impact.

• Implement offline evaluations using curated test datasets, golden answers, and regression test suites.

• Implement online evaluations, including user feedback loops, telemetry, and behavioral usage signals.

• Define evaluation thresholds, quality gates, and readiness criteria required for launch and scale.

• Partner with Product and RevOps leadership to ensure evaluation results inform release decisions, prioritization, and iteration.

Hallucination Reduction & Reliability

• Design and implement hallucination-reduction strategies in production systems, including:

• Retrieval-augmented generation (RAG) patterns

• Context filtering, grounding, and citation techniques

• Guardrails, validation checks, and response constraints

• Continuously monitor AI outputs, confidence signals, and failure modes in live environments.

• Investigate root causes of incorrect or low-confidence outputs and implement corrective improvements.

• Contribute to shared standards for explainability, traceability, and user trust in AI-assisted workflows.

Metrics, Telemetry & Impact Measurement

• Define and instrument metrics for AI systems, focused on outcomes rather than feature delivery.

• Track model and workflow performance across quality, adoption, latency, and reliability dimensions.

• Build telemetry that connects AI usage to downstream operational and revenue impact (e.g., cycle time reduction, capacity unlocked, risk reduced).

• Partner with analytics teams to ensure AI metrics are trustworthy, interpretable, and consistently applied across initiatives.

• Support post-launch measurement to validate realized impact within defined timeframes.

Data, Context & Governance

• Implement context pipelines that draw from structured data, documents, and governed knowledge assets.

• Support enterprise content readiness by consuming content through standardized pipelines rather than ad hoc document handling.

• Enforce data quality, access controls, grounding standards, and versioning in AI workflows.

• Support governance requirements including documentation, testing, auditability, and change management.

• Ensure compliance with privacy, security, and responsible AI guidelines across all implementations.

Cross-Functional Collaboration

• Work closely with RevOps AI Product Managers to understand intent, success criteria, adoption goals, and delivery priorities.

• Collaborate with AI Solutions Architects, Systems teams, and GTM stakeholders to ensure implementations align with real-world workflows.

• Support enablement and adoption efforts by improving system reliability, explainability, and usability.

• Participate in reviews and retrospectives to continuously improve delivery quality and operational impact.

Knowledge / Skills / Abilities

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