Principal AI Platform Engineer

Avaya

20h • $179,127 - $234,100 • Remote

About The Position

Avaya is seeking a hands-on Principal Engineer to lead reliable integrations and next-generation AI platform capabilities for Avaya Infinity™. The role bridges distributed systems engineering and machine learning operations (MLOps) to build a foundation for intelligent, agent-assisted workflows. The engineer acts as a systems architect and director of AI coding agents while retaining ownership of system design, security, reliability, and production behavior.

The position involves proactive ownership of technical initiatives from identifying architectural and operational gaps through delivery. It includes communicating technical trade-offs and blockers, sharing knowledge with peers, and remaining accountable for production systems by investigating failures, using user feedback, and improving performance and reliability.

The role designs and implements services that connect enterprise applications, data platforms, models, and tools; automates model and application lifecycle management; establishes security and recovery across system boundaries; and operates inference and agent services. It also leads agentic delivery across services, integrations, and release automation.

Requirements

• Hands-on production development experience in Go (Golang) or Rust, along with working proficiency in Python.
• Personal ownership of system design and implementation.
• Substantial systems integration experience, including the 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.
• 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.
• Ability to 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.
• Ability to independently understand, maintain, and debug code, and explain agent mistakes caught; independently established test expectations; security and performance checks; CI/CD; hardening; rollout; rollback; and verified production outcomes.
• Ability to learn unfamiliar systems with AI and apply that understanding after release.
• Bachelor's degree or equivalent experience.

Nice To Haves

• 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.
• 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).

Responsibilities

• Identify architectural and operational gaps, propose structured solutions, and own initiatives from concept through completion.
• Communicate technical trade-offs clearly, surface blockers early, and share knowledge with peers.
• Investigate production failures, synthesize user feedback, and continuously improve performance and reliability.
• 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, and share reusable practices.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal