Senior Engineering Consultant – Cloud & AI

Role Overview

• The VCP Far Edge Automation team builds and maintains the automation that manages the full lifecycle of Verizon's virtualized RAN (vRAN) infrastructure — powering the nation's largest, most reliable 5G network.
• Alongside our DevOps engineers, our AI engineers are responsible for building the intelligent layer of our automation platform: Gen AI agents, LLM-powered pipelines, and context-aware tooling that reduces manual effort, accelerates remediation, and makes our automation smarter over time.
• As a Senior AI Engineer, you'll design, build, and operationalize production-grade Generative AI agents and applications that integrate directly with our automation workflows and network infrastructure.
• You'll work closely with the team to take technical direction and deliver reliable, low-latency AI solutions that the broader team can depend on in production.
• Designing, developing, and deploying production-grade Gen AI agents and applications, with a focus on reliability, low latency, and real-world operability.
• Building and maintaining LangGraph agents and custom Python orchestration logic to power GenAI pipelines — enabling low-latency inference, context-aware decision-making, and multi-step agentic workflows.
• Integrate AI agents with internal data sources, Postgres databases, and REST API endpoints to give agents the context they need to act intelligently.
• Designing and optimizing data ingestion and preprocessing pipelines in Python to support LLM inference and grounding workflows (RAG, tool use, structured outputs).
• Collaborating with DevOps engineers to ensure AI agents and pipelines are deployable, observable, and maintainable within existing CI/CD and infrastructure frameworks.
• Instrument and monitor AI agent performance — tracking latency, reliability, failure rates, and accuracy — and own improvements to those metrics.
• Maintaining clear documentation: agent architecture designs, integration specs, prompt strategies, and operational runbooks.
• Staying current with the rapidly evolving LLM and agent tooling ecosystem and bring relevant advances back to the team.

Requirements

• Bachelor's degree or four or more years of hands-on work experience.
• Six or more years of relevant experience.
• Experience with a strong Python focus — clean, production-grade, testable code.
• Deep, hands-on experience with the Python ecosystem for AI/ML and data workflows (LangChain, LangGraph, LlamaIndex, or similar orchestration frameworks).
• Demonstrated experience building and deploying LLM-powered agents or applications in a production environment.
• Strong understanding of LLM concepts: prompt engineering, RAG, tool/function calling, context windows, structured outputs, and agent memory patterns.
• Experience integrating AI systems with relational databases (Postgres or equivalent) and REST APIs.
• Solid understanding of software engineering fundamentals: version control (Git), code review, testing, and documentation practices.
• Ability to work US Central Standard Time (CST) business hours (8:00 AM to 5:00 PM CT), which corresponds to 6:30 PM to 3:30 AM Indian Standard Time.
• Even better if you have:
• Hands-on experience with AI agent frameworks and developer tools — such as Claude Code, OpenAI Assistants, or similar agentic platforms — including building custom tooling on top of them.
• Experience with MLOps practices: model versioning, pipeline monitoring, experiment tracking, and production observability for AI systems.
• Familiarity with DevOps tooling — Ansible, Jenkins, GitLab CI — and comfort working alongside infrastructure automation engineers.
• Linux server experience and Shell scripting skills for deploying and debugging AI applications in server environments.
• Experience with containerization (Docker, Kubernetes) for deploying AI workloads.
• Exposure to telecommunications, network operations, or infrastructure automation use cases — experience applying AI to ops problems like anomaly detection, log analysis, or failure prediction.
• Familiarity with vector databases (pgvector, Pinecone, Weaviate, or similar) for semantic search and RAG pipelines.
• Experience with streaming or event-driven architectures (Kafka, Redis) for real-time AI agent integrations.

Tech Stack

• Ansible
• Docker
• Jenkins
• Kafka
• Kubernetes
• Linux
• Postgres
• Python
• Redis
• Shell Scripting