Sr. Software Engineer - Core Systems

Avaya

About The Position

Avaya is seeking a hands-on software engineer with strong system design and server-side engineering skills to work across a complex enterprise platform. The engineer will translate product and engineering goals into practical designs, implement critical functionality, and carry improvements through testing, deployment, and production operation. The position calls for broad technical judgment, curiosity about how components work together, and a willingness to investigate unfamiliar codebases and problems across service boundaries. Agentic AI development is central to Avaya’s approach. The engineer will use AI agents and other AI tools to investigate, prototype, and deliver work while personally understanding, reviewing, and verifying the systems built. The role includes end-to-end ownership from problem definition through production adoption and ongoing operation, hands-on technical leadership, and collaboration across engineering teams.

Requirements

Substantial hands-on software engineering experience, including demonstrated ownership of production systems and meaningful technical decisions.
Strong system design skills, including reasoning about interfaces, dependencies, data flows, scalability, reliability, security, and maintainability across multiple components.
Deep backend engineering skills in at least one relevant language, such as Go, Rust, Node.js/TypeScript, or Java, including effective debugging and performance investigation.
Practical understanding of distributed systems, concurrency, asynchronous communication, data consistency, persistence, and failure recovery.
Experience with automated testing, deployment, observability, and production troubleshooting.
Practical use of AI coding agents for substantial engineering work, with the ability to explain decisions, identify mistakes, and independently verify results.
Curiosity and demonstrated initiative to learn unfamiliar technologies and investigate beyond a single component or specialty.
Clear communication and effective collaboration across engineering teams.
Bachelor’s degree or equivalent experience.
Must be currently authorized to work in the United States without needing visa sponsorship now or in the future.

Nice To Haves

Experience with databases, caching, cloud platforms, containers, and messaging technologies such as Kafka, NATS, or comparable systems.
Production development in Rust or Go.
Full-stack delivery or meaningful work with client applications, SDKs, or modern frontend frameworks.
Experience improving systems under demanding availability, latency, throughput, or cost constraints.
Experience modernizing existing software and delivering changes across multiple components or teams.
Experience with event-driven systems using Kafka, NATS, RabbitMQ, or comparable technologies.
Experience in contact center, telecommunications, or other real-time enterprise applications.
AI-enabled application development involving tool integration, orchestration, or evaluations.

Responsibilities

Take responsibility for work from problem definition through implementation, production adoption, and ongoing operation.
Identify gaps, gather context, propose practical solutions, coordinate appropriately, and move work forward through ambiguity and setbacks.
Balance speed and quality, explain tradeoffs, verify outcomes, surface blockers early, and follow through on commitments.
Investigate production failures, incorporate feedback, and improve reliability and maintainability after release.
Share knowledge and credit, seek input from component owners, and strengthen the team’s ability to deliver.
Design across component boundaries by turning ambiguous needs into clear designs, evaluating tradeoffs, defining interfaces, and considering system-wide effects.
Build and improve robust server-side services with attention to correctness, performance, scalability, security, and operational visibility.
Trace requests, data, and failures across components, identify root causes, and work with component owners on lasting improvements.
Use AI tools to build prototypes, compare approaches, and validate technical feasibility before developing promising ideas into production-quality software.
Move between design and code by prototyping, writing critical implementation code, reviewing changes, and validating assumptions through tests and measurements.
Break work into clear tasks for AI agents, provide relevant context and constraints, review generated changes, and verify behavior, including failure scenarios.
Learn unfamiliar systems through code, documentation, experiments, AI tools, and collaboration.
Improve maintainability and delivery speed while managing compatibility, rollout, and operational risk.
Contribute across APIs, client integrations, and user interfaces when needed to deliver complete outcomes.
Provide hands-on technical leadership by communicating decisions, coordinating across teams, mentoring peers, and helping others deliver.