Overview

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

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.

Qualifications

• 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.
• 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.
• Cloud: GCP and/or AWS
• Languages: Java, Python, Go, and/or equivalent production engineering languages
• AI: LLMs, generative AI, agentic frameworks and architectures, model APIs, evaluation and observability tooling
• Platform & Infrastructure: Containers, Kubernetes, infrastructure as code, CI/CD, cloud-native services
• Engineering: APIs, distributed systems, event-driven architectures, observability and developer tooling
• Security: Cloud security, identity and access management, secrets management, secure application and AI architecture
• Drives Results: Action-oriented problem solvers who quickly bring clarity and simplicity to ambiguity, challenge the status quo, and lead meaningful change; celebrating wins to fuel momentum. They act swiftly and pragmatically, learning and improving as they go.
• Critical Thinker: Data-driven, forward-thinking individuals who identify key drivers, anticipate risks, and deliver clear recommendations. They confidently leverage AI and automation to reduce friction, improve decision-making, and focus on higher-value work.
• Right Attitude: Collaborative, competitive, and resilient team players who jump in to solve tough problems, learn from setbacks, and foster a culture of service, respect, and care for customers and teammates.
• Medical - Medical insurance coverage is available for employees, their spouse, and up to two dependent children with a limit of 500,000 INR, as well as their parents or in-laws for up to 300,000 INR. This comprehensive coverage ensures that essential healthcare needs are met for the entire family unit, providing peace of mind and security in times of medical necessity.
• Group Term & Group Personal Accident Insurance - Provides insurance coverage against the risk of death / injury during the policy period sustained due to an accident caused by violent, visible & external means.