Nextiva Staff AI Engineer at Nextiva designs and builds production-grade AI workflows to improve engineering and business processes. The role involves architecting scalable AI platforms and delivering measurable business impact through automation.
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.
Qualification
Hands-on experience working with LLMsStrong understanding of DevOpsStrong understanding of applicationTools & Tech Stack
Required
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.
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.
Experience building internal developer platforms, engineering productivity platforms, or enterprise automation capabilities.
Experience with AI evaluation frameworks, model/agent observability, prompt and context management, retrieval architectures, and production AI quality measurement.
Experience spanning traditional software engineering and AI engineering—for example, a strong backend, platform, or distributed-systems engineering foundation combined with substantial hands-on AI engineering experience.
Experience driving technical initiatives that span multiple teams, systems, or organizational domains.
Tools & Tech Stack
The exact stack will evolve as we select technologies based on the problems and outcomes we are solving. Relevant experience may include:
