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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in AI engineering and implementation, focusing on building and operating LLM-based workflows and agents. Proficient in designing evaluation frameworks and ensuring AI solutions meet quality, reliability, and operational standards.
Highest-signal resume keywords
AI EngineeringLLM-Based WorkflowsEvaluation FrameworksData PrivacyCross-Functional Collaboration
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI Solution ImplementationPrompt DesignContext EngineeringTelemetry ImplementationRegression TestingData Quality EnforcementHallucination ReductionOperational Metrics DefinitionProduction System OperationAutomation Engineering
Soft Skills
Analytical MindsetClear Communication
Tools & Technologies
SalesforceMicrosoft 365Azure AICopilotRevOps SystemsGTM ToolingBehavioral SignalsTelemetry ToolsStructured Data PipelinesKnowledge Management Systems
Industry Keywords
AI-Powered WorkflowsLLM EvaluationData Privacy TrainingResponsible AIChange Management
Tech Stack
Tools & technologiesAzure
About the role
Key responsibilities & impact- Build, test, deploy, and operate AI-powered workflows, agents, and automations within RevOps systems such as Salesforce, Microsoft 365, Azure AI, and Copilot
- Implement prompt logic, orchestration flows, tool calling, and context retrieval for LLM-based systems
- Translate approved AI solution designs into scalable, maintainable, and secure implementations
- Ensure AI solutions are production-ready, observable, resilient, and aligned with business outcomes
- Design and maintain LLM evaluation frameworks assessing accuracy, relevance, consistency, and outcome impact
- Implement offline evaluations with curated datasets, golden answers, and regression test suites
- Implement online evaluations using user feedback loops, telemetry, and behavioral usage signals
- Define evaluation thresholds, quality gates, and launch-readiness criteria
- Design and implement hallucination-reduction strategies using RAG, context filtering, grounding, citations, guardrails, validation checks, and response constraints
- Monitor AI outputs, confidence signals, and failure modes in live environments
- Investigate root causes of incorrect or low-confidence outputs and implement corrective improvements
- Define and instrument metrics for quality, adoption, latency, reliability, and operational or revenue impact
- Build telemetry connecting AI usage to downstream operational and revenue outcomes
- Implement context pipelines using structured data, documents, and governed knowledge assets
- Enforce data quality, access controls, grounding standards, and versioning
- Support documentation, testing, auditability, change management, privacy, security, and responsible AI requirements
- Collaborate with RevOps AI Product Managers, AI Solutions Architects, Systems teams, analytics teams, and GTM stakeholders
- Support enablement, adoption, reviews, retrospectives, and continuous post-launch improvement
Requirements
What you’ll need- 3+ years of experience in AI engineering and implementation
- Strong software, data, or automation engineering background with experience operating production systems
- Hands-on experience building and operating LLM-based workflows and agents
- Demonstrated experience designing and operating LLM evaluation frameworks
- Familiarity with offline evaluations using test datasets and regression frameworks
- Familiarity with online evaluations using user feedback, telemetry, and behavioral signals
- Experience reducing hallucinations in production AI systems using grounding, validation, and guardrails
- Strong understanding of retrieval-augmented generation (RAG), prompt design, and context engineering
- Familiarity with RevOps systems such as CRM, GTM tooling, and workflow platforms
- Strong analytical mindset and ability to debug complex AI system behavior
- Clear written and verbal communication skills across technical and non-technical audiences
- Experience deploying AI or automation systems into production environments
- Experience working with cross-functional product, operations, and systems teams
- Experience supporting AI quality, reliability, and evaluation in live systems
- Responsible for preserving the confidentiality, integrity, and availability of company information assets
- Responsible for ensuring data privacy and completing required privacy training
- No degree requirement stated; Granicus notes it does not have degree requirements for most roles
Benefits
Comp & perks- Remote-first company
- Employee Resource Groups to encourage diverse voices
- Coffee with Mark sessions with the CEO
- Microsoft Teams communities focused on wellness, art, furbabies, family, parenting, and more
- Special guests discussing issues impacting employees