Lead Automation Analyst

Translate Success problems into AI solution designs — what the agent does, what context it needs, what the success criteria look like

Build orchestrators and sub-agents inside ORA that route work across the right skills, models and knowledge sources

Author and maintain the skills, prompts and knowledge base entries that power your solutions

Design and instrument feedback loops from day one — thumbs up/down, logs, eval signals — so quality is measurable from launch

Deploy solutions into production inside ORA, monitor for quality drift and iterate based on real advisor and customer signal

Pre-call briefs: customer context, adoption signals, retention flags, prior-call summaries

In-call guidance: next-best-action prompts, recommendation flows, real-time troubleshooting context

Post-call grading and summaries: quality scoring, AI-generated recaps, CRM updates, sentiment signals

Transcription-driven follow-ups: customer updates, recap notes, task creation, escalation routing

Internal knowledge tools: SOP lookup, policy access, product-knowledge retrieval for advisors

Identify the platform capability gap — what ORA needs to do for your solution to ship

Request the feature from RevOps with clear requirements, acceptance criteria, edge cases and customer impact

Participate in design reviews, give technical input and validate the build against your use case

Integrate the new capability into your solution once it ships and report back on outcomes

Ingest evaluation signal from the Quality Analyst II — what is breaking, what is drifting, what needs to be designed differently

Prioritize fixes and new builds based on impact, evaluation data and Success leadership input

Communicate what you shipped and why to Success leadership and Enablement

Provide design context to QA II so new solutions can be evaluated effectively from day one

Define and document the patterns for how AI solutions get designed, tested and shipped inside ORA

Build the playbooks that make the next hire’s onboarding faster

Set the bar for solution quality, test coverage and observability before anything ships to advisors

Bring rigor to model selection, orchestration patterns and prompt architecture across the function

Stay current on LLM behavior, orchestration patterns, model releases and emerging AI tooling

Run small experiments with new models, techniques or evaluation approaches inside ORA

Share findings with the broader Success team, the Quality Analyst II and RevOps engineering