Staff Software Engineer - Infrastructure (AI Native)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Software Engineer - Infrastructure (AI Native) based in India. As a Staff Software Engineer, you will shape the infrastructure that powers enterprise AI applications, helping transform innovative solutions into reliable, production-ready systems. You will work across cloud platforms, Kubernetes, distributed systems, deployment pipelines, and observability to build secure, scalable, and cost-efficient infrastructure. A key focus will be designing and shipping AI agents that can execute real infrastructure and engineering workflows autonomously, with appropriate safeguards and controls. You will help establish robust engineering practices, anticipate operational risks, and improve reliability across complex systems. Working in a remote-first, globally distributed environment, you will combine hands-on technical leadership with architectural guidance and mentorship. This is an opportunity to influence how infrastructure and AI-driven engineering operations evolve at scale. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1 Accountabilities Design, build, and improve scalable cloud infrastructure spanning Kubernetes, networking, compute, storage, and distributed systems. Identify performance bottlenecks, potential failure modes, operational risks, and infrastructure weaknesses before they affect production environments. Strengthen CI/CD pipelines, automated testing, deployment controls, and rollout strategies to support safer, more reliable releases. Build and enhance observability capabilities, including monitoring, alerting, distributed tracing, and operational tooling that improves system visibility and troubleshooting. Optimize infrastructure performance, reliability, resource utilization, and cloud expenditure. Design, develop, and deploy AI agents for infrastructure management and engineering workflows, enabling them to use tools and APIs, execute multi-step tasks, troubleshoot issues, and automate operational activities. Establish safeguards for AI agents operating in production, including evaluation frameworks, permission controls, guardrails, observability, state management, and failure recovery mechanisms. Build and maintain internal platforms and developer tools that improve engineering productivity, such as package registries, artifact stores, CI systems, and infrastructure automation solutions. Lead technical design discussions and contribute to architectural decisions, design documentation, code reviews, and engineering standards. Mentor engineers, share technical knowledge, and help raise the quality of implementation and operational practices across the team. Participate in the on-call rotation, respond to production incidents, and translate incident learnings into permanent fixes, runbooks, and automation that reduce recurring issues. Collaborate across engineering teams to build infrastructure that supports evolving AI workloads and enterprise-scale production requirements. Requirements: Demonstrated experience designing, building, and shipping AI agents or agentic systems that perform meaningful, real-world tasks. Experience limited to AI coding assistants or prompt experimentation is not sufficient. Strong understanding of infrastructure fundamentals, including distributed systems, cloud computing, Kubernetes, networking, and production operations. Experience taking infrastructure from early development stages to meaningful production scale, with the ability to explain architectural choices, trade-offs, and lessons learned. A reliability-focused engineering mindset, with a strong understanding of failure modes, blast radius, operational complexity, resilience, and recovery strategies. Ability to troubleshoot complex issues across multiple layers, from application behavior and distributed services to Kubernetes, networking, and cloud infrastructure. Experience automating repetitive operational tasks and developing tools or platforms that improve the productivity of other engineers. Experience building internal developer platforms or infrastructure systems, such as CI pipelines, artifact storage, package registries, or related tooling. Strong technical communication skills, including the ability to produce clear design documents, explain architectural decisions, lead technical reviews, and document operational learnings. Staff-level technical leadership skills, including mentoring engineers, influencing engineering standards, and guiding complex projects without relying solely on formal authority. Practical understanding of AI agent architecture, including tool usage, workflow orchestration, state or memory management, evaluation, guardrails, and agent reliability. Experience with multi-tenant SaaS platforms, infrastructure automation, distributed systems, or production-grade AI agents is particularly valuable. Familiarity with technologies such as AWS/EKS, Kubernetes, OpenTofu, FluxCD, Temporal, VictoriaMetrics, ClickHouse, Grafana, Go, or Rust is beneficial. Expertise across the entire stack is not required, provided you have strong fundamentals and can learn new technologies quickly. Ability to work effectively in a remote-first, asynchronous environment, taking ownership of technical decisions and collaborating with colleagues across locations. Benefits: Competitive compensation: Market-benchmarked base salary, performance-based variable pay, and impact-driven equity for most roles. Comprehensive health coverage: Health, dental, vision, and mental health benefits from day one, alongside flexible health stipends tailored to the relevant country. Flexible time off: Flexible vacation arrangements and modern leave policies designed to support personal well-being and work-life balance. Remote-first flexibility: Work remotely with a globally distributed team spanning more than 15 countries, within a high-trust, asynchronous working environment. AI-native culture: Work in an environment where AI is integrated into everyday engineering practices, tools, and decision-making. Accelerated professional growth: Develop expertise through complex technical challenges, cutting-edge technologies, and large-scale implementations. Technical ownership: Opportunities to influence infrastructure architecture, shape engineering practices, and build systems that support the next generation of AI applications. Collaborative global team: Learn from experienced professionals, share knowledge, and contribute to a culture that values technical excellence, inclusion, and continuous improvement.