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Corporate Vice President - Release Train Engineer; RTE; Agentic AI Web Application

Job in New York, New York County, New York, 10261, USA
Listing for: New-York-Life
Part Time position
Listed on 2026-09-01
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), IT Project Manager
Salary/Wage Range or Industry Benchmark: 148000 - 211000 USD Yearly USD 148000.00 211000.00 YEAR
Job Description & How to Apply Below
Position: Corporate Vice President - Release Train Engineer (RTE) - Agentic AI Web Application
Location: New York

Job Description

Requisition ID94751

Department Tech Data AI Ventures Job Function Tech Data AI Ventures Location New  York,New York,United States Role Location Designation Hybrid - 3 days per week Location Designation:
Hybrid - 3 days per week Release Train Engineer (RTE) – Agentic AI Web Application

Role Overview We are seeking an experienced Release Train Engineer (RTE) to lead delivery of a next-generation Agentic AI web application. The RTE will serve as the delivery and execution leader across multiple agile teams responsible for building AI-powered user experiences, autonomous/agentic workflows, platform services, integrations, and production capabilities.

This role sits at the intersection of product, engineering, AI/ML, architecture, security, and operations, ensuring teams remain aligned on outcomes while managing the unique delivery risks associated with rapidly evolving generative and agentic AI technologies.

The ideal candidate combines strong SAFe/Agile program leadership with sufficient technical fluency to facilitate discussions involving LLMs, AI agents, APIs, cloud platforms, data, security, observability, and modern web architectures.

Key Responsibilities Agile Release Train Leadership
· Lead and facilitate the Agile Release Train (ART) across product, web engineering, AI/ML, platform, architecture, security, QA, and Dev Ops teams.
· Facilitate PI Planning, ART Syncs, Scrum of Scrums, system demos, Inspect & Adapt sessions, and dependency/risk reviews.
· Partner with Product Management and Architecture to translate product strategy into executable PI objectives and delivery plans.
· Maintain visibility into milestones, dependencies, risks, impediments, and cross-team commitments.
· Drive predictable delivery without sacrificing experimentation and learning required for emerging AI capabilities.
· Coach teams and leaders on Agile/SAFe practices and continuously improve ART effectiveness.

Agentic AI Delivery
· Coordinate delivery of capabilities involving LLMs, AI agents, tool/function calling, retrieval-augmented generation (RAG), orchestration, memory/context management, and human-in-the-loop workflows.
· Manage dependencies between AI capabilities and traditional application components such as frontend, backend services, APIs, identity, databases, and enterprise integrations.
· Help teams distinguish between AI experimentation, production engineering, and product commitments, creating appropriate delivery mechanisms for each.
· Coordinate evaluation and readiness criteria for AI capabilities, including quality, accuracy, latency, reliability, safety, and cost.
· Facilitate resolution of issues involving model dependencies, prompts, agent behavior, data availability, integrations, and platform constraints.

Release & Production Readiness
· Coordinate end-to-end release planning across development, testing, security, infrastructure, and operations.
· Establish clear release readiness criteria and ensure teams address critical dependencies before production deployment.
· Partner with Dev Ops/SRE teams to strengthen CI/CD, automated testing, observability, rollback strategies, feature flags, and production monitoring.
· Ensure releases account for AI-specific operational considerations such as model availability, token consumption, latency, hallucination risk, agent failures, and third-party AI service dependencies.
· Facilitate post-release reviews and ensure production learnings are incorporated into subsequent planning.

Metrics & Continuous Improvement Develop and maintain ART-level metrics covering:
· PI objective achievement
· Predictability and delivery confidence
· Feature/epic flow
· Cycle and lead time
· Dependency aging
· Defects and production incidents
· Release frequency
· Deployment success
· AI quality/evaluation results
· Reliability and latency
· AI/model usage and cost

Use metrics to identify systemic bottlenecks and facilitate measurable improvements rather than using metrics solely for status reporting.

Required Qualifications
· 10+ years of experience in Agile delivery, program management, technical program management, or engineering delivery leadership.
· 5+ years of experience functioning…
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