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Frontier Communications

Principal Forward Deployed AI Engineer

AI Application Engineering +1Posted on Nov 11
Full-time$120.5k - $231k

About the Job

Requirements

  • A bachelor's degree in Computer Science, Software Engineering, or a related technical field, or four or more years of work experience.
  • Six or more years of relevant experience, demonstrated through work and/or military experience or specialized training.
  • Extensive hands-on experience using frontier large language models, including Anthropic Claude, OpenAI, and Google Gemini, and agentic developer frameworks, including Claude Code, Cursor, and Copilot, to build scalable solutions and achieve business milestones.
  • Six or more years of shipping production software and/or demonstrated experience deploying large language model applications, agentic workflows, or autonomous tool-calling pipelines into production.
  • Proficiency in Python and modern TypeScript/Node.js for full-stack API integration and backend orchestration.
  • Experience with containerized deployments using Docker, API design, and cloud environments such as AWS, GCP, or Azure.
  • A proven track record implementing offline evaluation benchmarks and online evaluation and observability pipelines for latency, token costs, hallucination tracking, and evaluation-driven development.
  • Experience deploying containerized services and data workflows across enterprise platforms such as Domino Data Lab, Apache Airflow, Kubernetes (EKS/GKE), and hyperscaler cloud environments (AWS/GCP/Azure).
  • Advanced full-stack programming experience in Python, SQL, and modern JavaScript/TypeScript stacks.
  • A proven track record designing offline/online evaluation harnesses, guardrail frameworks, and observability systems for latency, token optimization, and tracing.
  • Ability to independently scope ambiguous technical initiatives and present architectural designs to senior executive stakeholders.
  • Ability to interface directly with enterprise stakeholders, translate ambiguous business requirements into technical architectures, and lead project delivery end-to-end.

Responsibilities

  • Define the full-stack architecture for embedded AI initiatives, including multi-agent orchestration, state machines, latency and cost budgets, security boundaries, and enterprise back-end integrations.
  • Architect stateful agent workflows that comply with established agentic governance rules, using LangGraph, Temporal, or event-driven state machines, and establish protocols for tool calling, execution sandboxing, and deterministic fallback logic.
  • Design production retrieval-augmented generation infrastructure incorporating hybrid search, re-ranking algorithms, metadata filtering, and semantic caching, informing and scaling Verizon's context architecture.
  • Deploy production agent services and scheduled workflows to target runtimes, including Domino Data Lab, Apache Airflow, Kubernetes microservices, and serverless compute across AWS, GCP, or Azure.
  • Architect offline and online evaluation frameworks, including large language model-as-a-judge, synthetic test generation, human-in-the-loop review queues, and live A/B rollouts, to measure model drift, context hallucination rates, and task completion metrics.
  • Generalize custom domain deployments into reusable internal software development kits, API primitives, and modular user interface components; mentor engineers across the organization; and draft technical documentation for Verizon patent disclosures.
  • Embed alongside business-unit leadership to map operational processes and identify high-leverage intervention points where applied AI can deliver measurable improvement.
  • Serve as the technical authority with business-unit vice presidents and managing directors, explaining algorithmic behavior, failure modes, and performance trade-offs.

Desired Qualifications

  • A master's degree in Computer Science, Machine Learning, Robotics, or a related technical field.
  • Experience building and scaling centralized context platforms or enterprise search engines similar to Verizon's internal enterprise context layer platforms.
  • Prior forward-deployed engineering, solutions architecture, or technical consulting experience in top-tier technology firms or hyperscalers.
  • A record of turning one-off operational builds into enterprise-wide shared libraries, with associated technical publications or filed patents.
  • Domain knowledge in telecommunications, network capacity optimization, capital planning, or large-scale customer care operations.
  • Expertise in retrieval and context architectures, including production retrieval-augmented generation pipelines, vector databases such as pgvector, Milvus, or Pinecone, hybrid search (dense and sparse/lexical), and dynamic context management.

About the Company

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