Agile Project Manager

Avetta is building the largest global community of hiring clients and suppliers that are Ready to Work. Its unified platform streamlines compliance, prequalification, safety and performance benchmarking in a single, integrated experience. Trusted by 360,000 businesses across more than 120 countries, Avetta blends AI-driven insights and human expertise to close risk gaps and strengthen supplier reliability so projects start on time, risks are managed proactively and operations scale with certainty.

SUMMARY 

Avetta is evolving beyond traditional Agile and SAFe delivery frameworks into an AI-Augmented Product Development Lifecycle (AI-PDLC) a structured pipeline where AI agents assist at every stage, from product discovery through to production release, while humans retain ownership of every key decision. 

As an Agile Project Manager (APM) in the AI-PDLC, you are the connective tissue of that pipeline. You don't just track progress, but you ensure the right human judgment is applied at the right gates, so AI-generated work meets Avetta's quality bar before it moves forward. 

This role is ideal for a mid-level Agile delivery professional who is confident running cross-functional initiatives, comfortable working directly with product and engineering pod teams, and genuinely curious about how AI is reshaping software delivery.

Candidates residing within 30 miles of an Avetta office are required to follow a hybrid work schedule of three days in the office and two days working from home each week. Candidates residing more than 30 miles from an Avetta office may be considered for remote employment.

ESSENTIAL DUTIES AND RESPONSIBILITIES:

• Pipeline Coordination & Flow Management 
  • Own end-to-end delivery flow for one or more initiatives across Avetta's 5-zone AI-PDLC pipeline — from validated discovery through to production release. 

• • Track initiative progress using the 3 Red Box milestone model (pipeline entry → approved spec → production), with cycle time and throughput as primary delivery metrics. 

• • Coordinate zone-to-zone handoffs, ensuring the right sign-offs are in place before any AI-generated artifact like spec, task breakdown, or code advances downstream. 

• • Facilitate the Spec Agreement gate: the checkpoint where both the Product Manager and Tech Lead confirm intent and approach before AI-assisted coding begins. 

• • Maintain the Gate Decision Log in Confluence for each initiative: what was reviewed, what was decided, and what changed: the audit trail proving human judgment drove every step. 

• • Flag missing or incomplete dependencies (such as the SDAP coding rulebook) early; no AI coding work moves forward without them. 

• Cross-Pod Dependency Orchestration 

• • Coordinate across 3–5 person delivery pods, acting as the integration point for work spanning multiple teams, domains, or codebases. 

• • Facilitate cross-pod syncs focused on AI pipeline blockers, spec quality issues, and zone-to-zone handoff readiness, replacing traditional Scrum of Scrums. 

• • Own RAID and decision logs at the initiative level, with specific attention to AI pipeline failure modes such as unresolved spec questions and AI output drift. 

• • Partner with Scrum Masters to surface and escalate pod-level blockers before they become pipeline bottlenecks. 

• Stakeholder Management & Communication 

• • Act as the primary communication point between Product Management, engineering pod leads, Release Management, and senior stakeholders for initiative status, risks, and key decisions. 

• • Prepare concise, data-driven updates for monthly C-level reporting, structured around pipeline volume, spec approval rate, and production throughput. 

• • Communicate spec quality issues and AI agent output concerns clearly, with proposed mitigations not just escalation. 

• • Help PMs set stakeholder expectations around AI-PDLC timelines, which may differ from traditional Agile estimates. 

• Spec Quality & Gate Integrity 

• • Apply the Spec Rubric (a pass/fail quality checklist) at the approved-spec gate to ensure no work is greenlighted below the agreed quality standard. 

• • Surface and report spec rejection patterns as early-warning signals — high rejection rates predict downstream delivery slowdowns. 

• • Ensure Definition of Ready criteria are met before any initiative enters the delivery zone. 

• • Track open spec questions across initiatives and ensure they are resolved — not bypassed. 

• Tooling, Metrics & Continuous Improvement 

• • Maintain delivery data in Jira, Aha!, and Confluence aligned to the AI-PDLC pipeline; log gate timestamps accurately for cycle time reporting.