AI Engineer - Go To Market

Verizon•San Diego, CA

1d•$120,500 - $231,000•Hybrid

About The Position

This role focuses on designing, deploying, and optimizing Generative AI solutions to improve Verizon's Go-To-Market (GTM) engine. The engineer will leverage advanced AI techniques like agentic workflows and Retrieval-Augmented Generation (RAG) with Foundation Models to create AI-native tools that enhance sales and marketing team performance. The goal is to move beyond traditional data analytics to build intuitive tools that supercharge revenue capabilities.

Requirements

Strong proficiency in Python/TypeScript and experience building web services or integrating complex APIs (especially CRM, VoIP, or sales engagement platforms).
Solid understanding of the LLM stack, including experience with orchestration frameworks (e.g., LangChain/LangGraph, Strands Agents, CrewAI).
Knowledge of modern AI concepts: Prompt Engineering, few-shot learning, and the difference between fine-tuning and RAG.
Experience with standard software development tools (Git, Docker) and CI/CD processes.
A product-oriented and revenue-focused mindset: the ability to deeply empathize with a BDR's workflow and design AI outputs that are instantly actionable during a live call.
Fearless 'Hacker' Mindset: Comfortable generating code with AI and crafting the remaining code with high precision.

Nice To Haves

Domain expertise in Go-To-Market, RevOps, or Sales tech stacks (Salesforce, Hubspot, Gong, Apollo, Clearbit).
Experience building real-time audio/speech-to-text processing pipelines (e.g., WebRTC, Deepgram, Whisper) for live transcription and analysis.
Contributions to open-source AI projects or a portfolio of 'weekend' prototypes built with an AI-native toolchain.
Exposure to cloud-based AI services (e.g., AWS Bedrock, Azure AI Studio, or GCP Vertex AI).
Experience with Vector Databases and semantic search concepts.
Experience with AI evaluation and observability tools.
Knowledge of 'agentic' design patterns.

Responsibilities

Building and refining LLM-powered features and autonomous agents to enhance revenue capabilities.
Designing and implementing RAG pipelines to connect proprietary data with large language models for accurate, context-aware outputs.
Exploring and eliminating GTM friction points by prototyping AI solutions for lead enrichment, personalized outreach, and CRM automation.
Integrating AI workflows into the modern sales stack (e.g., Salesforce, Gong) for seamless adoption by sales representatives.
Developing and maintaining production-grade infrastructure for AI orchestration, including prompt management, low-latency streaming APIs, and model evaluation frameworks.
Rapid prototyping using tools like Claude Code, Codex, Lovable, and LangSmith Agent Builder to quickly deploy full-stack MVPs.