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

New York Life•New York, NY

47d•$147,500 - $211,000•Hybrid

About The Position

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.

Requirements

10+ years of experience in Agile delivery, program management, technical program management, or engineering delivery leadership.
5+ years of experience functioning as an RTE, Senior Scrum Master, Agile Program Lead, or equivalent cross-team delivery leader.
Demonstrated experience coordinating multiple engineering teams delivering complex enterprise applications.
Strong knowledge of SAFe, Scrum, Kanban, Agile planning, dependency management, and release management.
Experience working with modern web/application architectures, APIs, cloud platforms, CI/CD, and DevOps practices.
Working knowledge of Generative AI and LLM-based application architectures.
Ability to facilitate technical conversations among AI engineers, software engineers, architects, product managers, security teams, and business stakeholders.
Strong executive communication, facilitation, conflict resolution, and stakeholder-management skills.
Proven ability to identify systemic impediments and drive resolution across organizational boundaries.

Nice To Haves

SAFe Release Train Engineer (RTE), SAFe Practice Consultant (SPC), or equivalent certification.
Experience delivering Generative AI or Agentic AI applications.
Familiarity with concepts such as: LLMs and foundation models, AI agents and multi-agent architectures, Prompt engineering and prompt management, Tool/function calling, RAG and vector search, Agent orchestration, AI evaluation frameworks, Guardrails and Responsible AI, AI observability, Model/token cost management.
Experience with public cloud and AI platforms such as Azure, AWS, or Google Cloud.
Experience delivering applications in a regulated enterprise environment.
Familiarity with modern web architectures, microservices, event-driven systems, API ecosystems, and enterprise identity/security.

Responsibilities

Lead and facilitate the Agile Release Train (ART) across product, web engineering, AI/ML, platform, architecture, security, QA, and DevOps 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.
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.
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 DevOps/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.
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.