Staff AI Software Engineer

Redefine the future of customer experiences. One conversation at a time.

At Nextiva, we’re reimagining how businesses connect, bringing together customer experience and team collaboration on a single, conversation centric platform. Powered by AI, driven by human innovation.

Our culture is forward thinking, customer obsessed and built on the belief that meaningful connections drive better business outcomes. Whether it’s through our signature Amazing Service®, the technology we create, or the experiences we cultivate, connection is at the core of who we are.

If you’re ready to collaborate with incredible people, make an impact, and help businesses everywhere deliver truly amazing experiences, this is where you belong.

Location: This is an onsite role based at Nextiva’s Bengaluru office (Wilshire III by MFAR, 492, Hobli, RHB Colony, Mahadevapura, Bengaluru, Karnataka 560048). Working together onsite strengthens how we operate, enabling faster decisions, clearer communication, and stronger execution, so you can make a greater impact and move work forward with speed and clarity.

In-Office Expectation: This role is expected to work onsite four days per week, with the potential to increase to five days per week, as required by the business. Specific scheduling and flexibility will be guided by your leader to support both team collaboration and individual productivity.

Nextiva is looking for a Staff AI Engineer to help build an internal AI platform that will shape how we work across engineering and the broader organization.

This is a highly technical, hands-on role at the intersection of software engineering, system architecture, AI engineering, cloud infrastructure, and developer productivity. A key focus will be designing and building agentic workflows across the software development lifecycle (SDLC), alongside intelligent automation that improves engineering and business processes across Nextiva.

The scope extends across the breadth of Nextiva’s technology platform and organization rather than being tied to a single product or domain. You will identify high-value opportunities, architect solutions, build production-quality systems, and take them through deployment, operationalization, security, observability, and scale.

We are looking for an engineer who understands that AI is a means to a business outcome—not the outcome itself. You will bring strong technical judgment to decisions about when and how AI should be applied and will be accountable for turning promising capabilities into reliable systems that create measurable value.

Top 3 Outcomes (first 12 months)

• Build and operationalize agentic SDLC capabilities: Architect and deliver production-grade AI workflows that materially improve how Nextiva engineers design, build, test, review, deploy, operate, and maintain software, with measurable improvements in engineering effectiveness.

• Establish a scalable internal AI platform: Develop reusable architecture, services, infrastructure, patterns, and guardrails that enable AI-powered workflows to move reliably from experimentation into secure, observable, production-scale operation.

• Deliver measurable business impact through AI and automation: Identify and execute high-value automation opportunities across engineering and business processes, establishing clear success metrics and demonstrating tangible improvements in productivity, quality, velocity, cost, or customer outcomes.

Key Responsibilities

• Architect, build, deploy, and operate production-grade AI systems and agentic workflows across the SDLC and broader business processes.
• Design scalable internal platform capabilities that enable teams across Nextiva to safely and efficiently adopt AI-powered automation.
• Own solutions end-to-end—from problem definition and architecture through implementation, deployment, observability, security, scaling, and ongoing operation.
• Build robust integrations between AI models, agents, developer tooling, internal platforms, enterprise systems, APIs, data sources, and engineering workflows.
• Apply strong software engineering principles to AI systems, including modular architecture, testing, reliability, performance, maintainability, and fault tolerance.
• Design and implement approaches for AI evaluation, observability, quality measurement, failure handling, and continuous improvement in production.
• Make pragmatic architectural and technology decisio…