Technical AI Project Manager

About Patra

Patra is an outcome-focused operating partner working exclusively for the insurance industry. Combining expert global teams, process intelligence, and purpose-built technology, Patra delivers measurable results to clients. With 20 years of deep domain expertise, exceptional client retention rates, and significant sustained investment in AI, Patra partners with agencies, brokers, MGAs, MGUs, and carriers to transform how insurance operations are delivered.

Core Duties

The Technical Project Manager will drive the AI Development Lifecycle (AI‑DLC) where AI agents contribute across development and quality processes, and Human‑in‑the‑Loop (HITL) engineers govern their output. Acting as delivery lead, change agent, and AI practitioner, this role adapts delivery rituals, sets governance and review standards, and embeds AI tooling into daily workflows to keep the human layer effective and accountable.

Working with Product and Engineering counterparts, the Technical Project Manager will act as the Project Management leader to create, implement and improve delivery models and drive development outcomes.

Summary

Responsible

Lead adoption of the AI Development Lifecycle and HITL delivery, and develop governance, policy, and tooling to ensure scalable, auditable human oversight of agent‑augmented work. Keep current with rapid advancements in software development to drive continuous improvements in the AI-DLC.

Accountable

In partnership with Product and Engineering, drive development delivery outcomes, compliance, auditability, and the integrity of human oversight and governance for agent‑contributed work.

Consultative Management and Oversight

Coach HITL leads and stakeholders, develop policy and governance artifacts, align cross‑functional engagement models, and translate delivery metrics into actionable guidance to scale repeatable, auditable hybrid human‑agent delivery.

Quantitative Measurements

· Success Criteria Management

· CSAT for Product and Engineering

· Audit pass rate/compliance score

· Time to Restore Service (MTTR)

· Lead Time from Idea to Delivery

· Token cost per deliverable

· Agent autonomy

· Code quality

Core Management Responsibilities – Across All Service Lines

• Strategic direction — drive roadmap execution for AI‑DLC and HITL adoption across teams and client engagements.
• Outcomes accountability — ensure and track client outcomes from engineering projects. Projects are primarily agentic and require AI fluency to manage.
• Governance & policy — contribute and enforce HITL governance, review standards, audit cadence, and compliance requirements.
• People leadership & coaching — mentor engineers, product owners, and cross‑functional stakeholders on hybrid delivery practices and responsible AI use.
• Stakeholder alignment — coordinate with Sales, Client Success, Security, and Finance to align delivery with commercial and risk objectives.
• Performance measurement — set KPI framework (DORA/SPACE adapted for HITL), establish targets, and translate metrics into executive narratives.
• Vendor & partner oversight — set briefs, success metrics, and quality standards for external partners and managed services.

Core Operational Responsibilities

• Program delivery management — plan, prioritize, and oversee end‑to‑end delivery across teams, managing scope, schedule, risks, and dependencies. Data driven reporting is an essential foundation.
• Tooling ownership — administer Jira and Confluence configurations, automation rules, templates, and data hygiene to reflect HITL workflows.
• Quality assurance & incident response — monitor change failure rate, MTTR, and remediation actions; ensure human oversight on agent‑contributed work.
• Automation & scale — design and implement automation to reduce manual review where safe, and scale repeatable delivery motions.
• Reporting & dashboards — produce weekly/monthly dashboards (deployments, lead time, human review pass rate, audit status) for teams and leadership.
• Audit & compliance execution — run audits, document findings, and implement remediation to maintain traceability and regulatory readiness.

Key Performance Indicators

• Deployment Frequency — number of production deployments per period.
• Lead Time for Changes — median time from commit to production.
• Change Failure Rate — % of deployments causing incidents or rollbacks.
• Time to Restore Service (MTTR) — median time to recover from incidents.
• Team Satisfaction — Team satisfaction for Product and Development.
• Human Review Pass Rate — % of agent‑contributed outputs accepted without rework after human review.
• Automation Coverage — % of validation/review steps automated vs manual.
• Agent Autonomy - % of agent vs human time.
• Token Cost controls – staying within allocated token budgets.
• Audit / Compliance Pass Rate — % of audits passed or compliance checklist score.
• Jira/Confluence Data Hygiene — % of tickets with required fields; % of stale tickets closed.
• Operational Efficiency — reduction in manual review cycles; % improvement in lead time or MTTR over baseline.

Qualifications & Experience (Required):

• 8+ years of project or program management experience within a software development organization including experience in distributed engineering, data science, and operations.
• First-hand experience setting up and managing agentic development processes in a commercial setting with Claude Code or Codex, with the resulting code working in production.
• Demonstrated project experience with LLM/agentic systems delivered to production
• Proven experience coaching and leading teams through process or structural transitions.
• Daily practical fluency with LLM tools (Claude, Gemini, ChatGPT, or equivalent) and ability to teach others.
• Hands‑on Jira administration: workflows, automation, dashboards, advanced roadmaps.
• Confluence administration: space architecture, templates, permissions, and content governance.
• Working knowledge of DORA metrics and the SPACE framework and experience operationalizing outcomes‑based KPIs.
• Comfort with HITL models and environments where AI agents contribute under human oversight.
• Strong facilitation, communication, and stakeholder management skills.
• Demonstrated audit experience with regulatory bodies (ISO or equivalent)

Preferred (Nice-to-Haves):

• Familiarity with engineering intelligence platforms (Jellyfish, LinearB, Waydev, Faros.ai)
• Experience in regulated industries (insurance, financial services)
• Hands‑on experience with GitHub/GitLab or complementary developer tooling
• Background working with managed services or external delivery partners
• Practical experience in prompt engineering and LLM workflow design

Equal Employment Opportunity

Patra Corporation is an equal opportunity employer committed to celebrating diversity and creating a safe and inclusive environment for all employees.