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Frontier Communications

Distinguished Forward Deployed AI Engineer

AI Application EngineeringPosted on Nov 11
Full-time$137k - $263k

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.
  • Eight 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, or Google Gemini, and agentic developer frameworks such as Claude Code, Cursor, or Copilot to build scalable solutions and achieve business milestones.
  • Eight or more years shipping production software and/or demonstrated experience deploying large language model applications, agentic workflows, or autonomous tool-calling pipelines into production.
  • Strong 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.
  • Deep expertise designing production retrieval-augmented generation pipelines, vector databases such as pgvector, Milvus, or Pinecone, hybrid search, and dynamic context management.
  • Proven experience deploying containerized services and data workflows across enterprise platforms such as Domino Data Lab, Apache Airflow, Kubernetes (EKS/GKE), and hyperscaler cloud environments.
  • Advanced full-stack programming experience and mastery of Python, SQL, and modern JavaScript/TypeScript stacks.
  • A proven track record designing offline and 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

  • Lead the design and definition of full-stack architecture for embedded AI and agentic 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 governance rules using LangGraph, Temporal, or event-driven state machines, and establish protocols for tool calling, execution sandboxing, and deterministic fallback logic.
  • Lead the design of production retrieval-augmented generation infrastructure incorporating hybrid search, re-ranking algorithms, metadata filtering, and semantic caching, informing and scaling the company’s context architecture.
  • Deploy production agent services and scheduled workflows to runtimes such as 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 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 behaviors, 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 the company’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, customer growth and retention, customer segmentation, or large-scale customer care operations.

About the Company

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