Senior Manager - AI Engineering & Enterprise Strategy

Cellco Partnership; Basking Ridge (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

Verizon is seeking a senior engineering leader to architect and govern autonomous AI systems at scale. You will drive agent evaluation pipelines, RL optimization, and LLM fine-tuning in a Fortune 20 environment, partnering with VP/SVPs to translate technical realities into deployment decisions.

You will lead a team of Principal AI Engineers to execute this vision across enterprise AI platforms. You will build real-time observability, integrate with cloud governance, and ensure cost-efficient

Qualifications

• Bachelor's degree or six+ years of work experience.
• Ten+ years of relevant AI/ML engineering experience.
• Four+ years of experience in AI/ML engineering (LLMOps, model eval, fine-tuning, distributed systems).
• Three+ years of managing engineering/AI data science teams.
• Hands-on experience architecting AI evaluation frameworks and model optimization pipelines.
• Proven ability to optimize LLM inference costs and throughput.

Responsibilities

• Architect and build core systems for continuous AI agent evaluation and real-time observability across the enterprise.
• Drive RL techniques, model tuning, and throughput improvements to reduce inference costs.
• Lead fine-tuning of LLMs to increase system throughput while maintaining accuracy.
• Design and deploy lightweight models to intercept, triage, and reduce expensive LLM calls.
• Mentor Principal AI Engineers and Scientists, setting technical roadmaps.

Skills

AI architecture

ML engineering

RL optimization

LLMOps

multi-agent systems

cost optimization

team leadership

Education

Bachelor's degree

Master's or PhD (preferred)

Tools

LangGraph

AutoGen

MCP

AWS

GCP

Job description

When you join Verizon You want more out of a career. A place to share your ideas freely - even if they're daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love - driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together - lifting our communities and building trust in how we show up, everywhere & always.

Why Verizon? Most enterprise AI leaders are still helping teams build human-assisted copilots and isolated prototypes. At Verizon, you'll define the technical engineering strategy and architectural blueprint for deploying real-time, fully autonomous AI across hundreds of mission‑critical applications. Leading a specialized team of Principal AI Engineers and Scientists, you will architect the systems that validate, observe, evaluate, and optimize our autonomous voice and workflow agents for production scale.

This is a deep engineering role focused on building the underlying machinery for agentic AI governance. You will drive advanced optimization strategies-including reinforcement learning (harness and weights tuning) and LLM fine-tuning-to maximize system throughput while aggressively driving down inference costs. If you're a technical AI leader who wants to architect how a Fortune 20 enterprise safely and efficiently scales autonomous systems from end to end, this is your seat at the table.

What you'll be doing...

You are the engineering leader driving the architecture of Verizon's AI agent evaluation, optimization, and governance systems. You have deep technical AI architecture and ML engineering experience, combined with a passion for building scalable, high-throughput systems. While you will interact with VP and SVP-level executives to translate technical realities into go/no-go deployment decisions, your primary focus is hands‑on engineering leadership: designing the pipelines that make our AI faster, cheaper, and safer. You will lead a team of highly talented Principal AI Engineers and Architects to execute this vision.

In this role, you will....

• Architect and build the core engineering systems for continuous AI agentic evaluation, validation, and real-time observability across the enterprise.
• Drive advanced AI optimization strategies, including Reinforcement Learning (RL) techniques, harness tuning, and model weights tuning to improve agent accuracy and reliability.
• Lead the fine-tuning of LLMs specifically to increase system throughput and drastically reduce inference costs for agentic evaluation and execution pipelines.
• Design and deploy lightweight classification models (e.g., JEV) and semantic routing architectures to intercept, triage, and reduce expensive frontier LLM calls.
• Direct and mentor a specialized team of Principal AI Engineers and Scientists, planning technical roadmaps and ensuring production data science and ML code is scalable and maintainable.
• Integrate your custom evaluation architectures and real-time telemetry systems directly with AI registration hubs (AEGIS, KAIROS) and cloud governance frameworks (AWS/GCP) to automate strategic go/no-go deployment gates.
• Partner closely with AI Platform Engineering leaders to seamlessly embed your evaluation and optimization frameworks into the standard developer workflow.

You'll need to have...

• Bachelor's degree or six or more years of work experience.
• Ten or more years of relevant experience required, demonstrated through one or a combination of work and/or military experience, or specialized training.
• Four or more years of experience in AI/ML engineering, specifically focusing on LLMOps, model evaluation, fine-tuning pipelines, and distributed systems architecture.
• Three or more years of managing high-performing engineering/AI data science teams.
• Hands-on experience architecting AI evaluation frameworks and model optimization pipelines (e.g., Reinforcement Learning, RLHF/RLAIF, weight tuning).
• Proven ability to optimize LLM inference costs and throughput, including experience utilizing smaller classification models to route traffic or reduce large-model dependencies.
• Experience designing and shipping production-grade multi-agent frameworks (e.g., LangGraph, AutoGen, MCP) alongside the deterministic guardrails, semantic firewalls, and real-time circuit breakers required to enforce accuracy without a human in the loop
• .

Even better if you also have...

• Masters or PhD in Computer Science, Machine Learning, or AI Science.
• Deep hands-on experience with modern LLM fine-tuning techniques (LoRA, QLoRA) and automated evaluation metrics (e.g., LLM-as-a-judge).
• Proven track record of executive influence, serving as a strategic bridge between deep technical engineering teams and senior leadership (VP/SVP level) across Legal,