Staff AI Software Engineer

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. 

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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 Staff-level, hands-on individual contributor role at the intersection of backend/platform engineering, system architecture, AI, cloud infrastructure, and developer productivity. You’ll design and build production-grade agentic workflows across the software development lifecycle (SDLC) and intelligent automation that improves engineering and business processes across Nextiva.

Working across Nextiva’s technology platform, you’ll identify high-value opportunities, architect solutions, and take them from concept through deployment, operationalization, security, observability, and scale. You’ll also provide technical leadership and influence across engineering teams.

We are looking for an engineer who understands that AI is a means to a business outcome—not the outcome itself and can turn promising capabilities into reliable systems that create measurable value.

Top 3 Outcomes (first 12 months)

1. 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.
2. 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.
3. 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 approaches for AI evaluation, observability, quality measurement, failure handling, and continuous improvement in production.
• Make pragmatic architectural and technology decisions based on business outcomes, engineering constraints, risk, and total cost of ownership rather than technology trends.
• Work deeply within GCP and/or AWS, designing cloud-native architectures and infrastructure capable of supporting secure, reliable, scalable production workloads.
• Partner with infrastructure, DevOps, security, architecture, and engineering teams to establish reusable engineering patterns, technical standards, and operational guardrails for AI-powered systems.
• Understand complex software systems and large codebases to identify opportunities where AI can improve developer workflows across the end-to-end SDLC.
• Evaluate emerging AI models, frameworks, agent architectures, and developer technologies to determine where they can create meaningful value for Nextiva.
• Provide Staff-level technical leadership through architecture reviews, design decisions, technical mentorship, and influence across engineering teams.
• Translate ambiguous business and engineering problems into clear technical strategies and executable solutions, measuring and optimizing impact based on production data, user feedback, and business results.

Requirements

Must-have

• 10+ years of professional software engineering experience, with demonstrated experience designing, building, and operating complex production-scale systems.
• Demonstrable hands-on experience designing and building AI/ML or generative AI solutions that have reached production and/or delivered measurable business outcomes.
• Strong software architecture and system design skills, including experience making consequential technical decisions for distributed or large-scale systems.
• Strong programming and software engineering fundamentals in one or more production languages such as Java, Python, Go, or equivalent.
• Hands-on experience working with LLMs, generative AI, AI agents, or AI-enabled application architectures, beyond simply consuming AI productivity tools.
• Experience taking AI solutions beyond prototypes into deployment, operationalization, monitoring, security, reliability, and scale.
• Deep hands-on experience with GCP and/or AWS, including designing and operating cloud-native production systems—not solely familiarity with cloud services or consoles.
• Strong understanding of DevOps, CI/CD, infrastructure as code, observability, reliability, and production operations.
• Strong understanding of application, infrastructure, data, and AI security considerations and the ability to incorporate appropriate controls into system architecture.
• Strong understanding of the end-to-end software development lifecycle, developer workflows, and the challenges of working within large software systems and codebases.
• Ability to evaluate technical trade-offs and select technologies based on the problem, desired business outcome, operational requirements, and long-term maintainability.
• Strong technical judgment and the ability to operate effectively in ambiguous, cross-functional problem spaces.
• Ability to influence technical direction across teams without relying solely on organizational authority.

Preferred

• Experience designing or implementing agentic AI systems, multi-step AI workflows, tool-using agents, or autonomous/semi-autonomous engineering workflows.
• Experience applying AI to developer productivity, software engineering, code generation, testing, code review, incident management, or other stages of the SDLC.<