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# Principal AI Platform Engineer

- **Company:** Avaya Inc.
- **Location:** United States (Remote available)
- **Salary:** $179,127 - $234,100
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Application Lifecycle Management, Application Release Automation, Automation of Tests, Microsoft Azure, Continuous Integration, Cursor, Software Debugging, Distributed Systems, Python (Programming Language), Message Oriented Middleware, Systems Integration, Management of Software Versions, Enterprise Software Applications, Large Language Models, Concurrency, Multi-Cloud, AI Coding Agents, Agentic-AI, AI Platforms, Data Management, Machine Learning Operations, Api Management, Databricks, Golang
- **Published:** November 10, 2026
- **Apply:** https://careers.avaya.com/talentcommunity/apply/1455818533/?locale=en_US

## About the Role

* Hands-on production development in Go (Golang) or Rust and working Python proficiency, with personal ownership of system design and implementation.
 * Substantial systems integration experience. Ability to explain API contracts, compatibility, concurrency, asynchronous behavior, retries, idempotency, authentication, and failures across distributed services.
 * Production MLOps experience with reproducible training or model packaging, artifact tracking, deployment, monitoring, and rollback. Experience supporting batch or online inference and ML or AI workloads on Databricks.
 * Production delivery on GCP or Azure using automated tests, CI/CD, identity controls, and observability. Personal responsibility for troubleshooting and improving services after release.

Required Agentic Development Experience

 * Demonstrated delivery of a substantial feature, service, integration, or engineering tool from proof of concept into production using coding agents such as Codex, Cursor, or equivalents.
 * Decompose work, provide repository context and constraints, coordinate parallel tasks across files and components, maintain reusable agent instructions, and critically review and integrate generated changes.
 * Independently understand, maintain, and debug the code. Explain agent mistakes caught, tests with independently established expectations, security and performance checks, CI/CD, hardening, rollout, rollback, and verified production outcomes.
 * Learn unfamiliar systems with AI and apply that understanding after release. Tool familiarity, autocomplete, or prototype-only work does not establish this experience.

Preferred Experience

 * Advanced LLM Orchestration: Hands-on experience designing and operating LLM provider integrations, multi-model routing systems, runtime usage and cost controls, semantic drift monitoring, and evaluation-driven release pipelines.
 * Robust Runtime Architectures: Deep expertise building agent runtimes, orchestrating durable workflow engines, and delivering highly concurrent production systems using both Go and Rust across multi-cloud environments (GCP and Azure)., Bachelor degree or equivalent experience, Applicants must be currently authorized to work in the United States without the need for visa sponsorship now or in the future.

## Description

We are seeking a high-performing, hands-on Principal Engineer to lead the development of reliable integrations and next-generation AI platform capabilities for Avaya Infinity . In this role, you will bridge the gap between distributed systems engineering and machine learning operations (MLOps) to build a robust foundation for intelligent, agent-assisted workflows.

Agentic development is at the absolute core of our engineering philosophy. You will not just write code; you will act as a systems architect and director, guiding AI coding agents to implement solutions while you retain ultimate ownership of system design, security, reliability, and production behavior.

Ownership and Delivery

 * Proactive Problem Solving: Actively identify architectural and operational gaps, propose structured solutions, and assume end-to-end ownership to drive initiatives from concept to successful completion.
 * Transparent Communication: Articulate technical trade-offs with clarity, surface project blockers early to prevent timeline slips, and actively share knowledge to elevate the technical capabilities of the peer team.
 * Post-Release Accountability: Maintain steadfast accountability for code in production by investigating system failures, synthesizing user feedback, and continuously iterating to improve performance and reliability outcomes., * Design and implement services connecting enterprise applications, data platforms, models, and tools through APIs and asynchronous messaging.
 * Automate model and application lifecycle management, including versioning, reproducibility, evaluated promotion, controlled rollout, and rollback.
 * Implement authentication, permissions, isolation, and recovery across system boundaries while evolving interfaces without breaking consumers.
 * Work with AI engineers on release quality and operate inference and agent services, improving availability, latency, and cost.
 * Lead agentic delivery across services, integrations, and release automation, sharing reusable practices.