Technical AI Project Manager

Patra Corporation•UNAVAILABLE, UNAVAILABLE

About The Position

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.

Requirements

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).

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

Responsibilities

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.
Act as delivery lead, change agent, and AI practitioner, adapting delivery rituals, setting governance and review standards, and embedding AI tooling into daily workflows.
Collaborate with Product and Engineering counterparts to create, implement, and improve delivery models and drive development outcomes.
Lead adoption of the AI Development Lifecycle and HITL delivery, and develop governance, policy, and tooling for scalable, auditable human oversight of agent‑augmented work.
Stay current with advancements in software development to drive continuous improvements in the AI-DLC.
Drive development delivery outcomes, compliance, auditability, and the integrity of human oversight and governance for agent‑contributed work.
Coach HITL leads and stakeholders, develop policy and governance artifacts, align cross‑functional engagement models, and translate delivery metrics into actionable guidance.
Drive roadmap execution for AI‑DLC and HITL adoption across teams and client engagements.
Ensure and track client outcomes from engineering projects, managing agentic projects with AI fluency.
Contribute and enforce HITL governance, review standards, audit cadence, and compliance requirements.
Mentor engineers, product owners, and cross‑functional stakeholders on hybrid delivery practices and responsible AI use.
Coordinate with Sales, Client Success, Security, and Finance to align delivery with commercial and risk objectives.
Set KPI framework (DORA/SPACE adapted for HITL), establish targets, and translate metrics into executive narratives.
Set briefs, success metrics, and quality standards for external partners and managed services.
Plan, prioritize, and oversee end‑to‑end delivery across teams, managing scope, schedule, risks, and dependencies.
Administer Jira and Confluence configurations, automation rules, templates, and data hygiene.
Monitor change failure rate, MTTR, and remediation actions; ensure human oversight on agent‑contributed work.
Design and implement automation to reduce manual review and scale repeatable delivery motions.
Produce weekly/monthly dashboards for teams and leadership.
Run audits, document findings, and implement remediation to maintain traceability and regulatory readiness.