Distinguished Engineer - AI/ML Engineering

Verizon•Basking Ridge, NJ

7d•Hybrid

About The Position

Verizon is driving toward a fully autonomous network. We are looking for a technical leader to architect and build device-side data telemetry, GenAI-powered agents, and predictive analytics platforms. You will create proactive troubleshooting agents and "digital employees" that transform raw device and network telemetry into real-time operational intelligence, powering Verizon's autonomous network vision.

Requirements

Bachelor's degree or four or more years of work experience.
Eight or more years of relevant experience in AI/ML engineering, software architecture, and real-time data analytics.
Experience with Generative AI concepts, including LLMs, prompt engineering, RAG, and agentic multi-agent orchestration frameworks.
Experience building, deploying, and scaling data analytics and AI solutions across cloud environments, specifically AWS and GCP.
Strong background in device-side processing, low-latency edge deployment, or resource-constrained model optimization.

Nice To Haves

Master’s degree or PhD in Computer Science, Data Science, or a related technical field.
Experience with cloud AI ecosystems and frameworks (e.g., AWS Bedrock, Google Vertex AI, BigQuery, LangChain).
Understanding of 5G infrastructure, network(4G/5G) protocols, telemetry counters, and operational performance metrics.
Strong production coding capabilities with Python, SQL, Kafka, Spark, and microservice architectures for real-time streaming telemetry.
Deep domain expertise in telecommunications (5G RAN, Core, Transport, 5G signaling).
Experience implementing MLOps/LLMOps pipelines, security guardrails, and model evaluation standards for autonomous systems.

Responsibilities

Designing, engineering, and deploying edge-side telemetry pipelines and AI/ML models on devices to collect, process, and analyze real-time performance signals, counters, and event logs.
Architecting and operationalizing GenAI-powered agents, LLMs, retrieval-augmented generation (RAG), and agentic frameworks to function as digital employees capable of proactive troubleshooting, root cause analysis (RCA), and automated decision-making.
Building and scaling data analytics and streaming pipelines across cloud platforms (AWS, GCP) to handle high-throughput network and device telemetry.
Driving proactive fault detection, anomaly detection, and closed-loop remediation workflows to minimize network downtime and optimize 5G network performance.
Partnering with network engineering, data science, and product teams to translate complex network pain points into scalable, production-grade AI solutions.
Establishing end-to-end MLOps/LLMOps practices, including real-time model monitoring, fine-tuning, security guardrails, and automated CI/CD deployment pipelines.