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As an AI-Native Cloud Software Engineer, you won't just manage environments; you will build the software engines, intelligent pipelines, and autonomous systems that power our cloud presence. We are shifting from rigid configuration management to AI-driven, self-healing software architectures.
Job Responsibility
Multi-Cloud Generative IaC & Software-Defined Infrastructure: Architect and maintain scalable cloud systems across AWS, Azure, and GCP using Pulumi, AWS CDK, or Terraform
Integrate AI development workflows and custom LLM agents to accelerate safe infrastructure compilation, drift detection, and automated cross-cloud refactoring
Intelligent Automation & Agentic Workflows: Engineer custom software utilities, internal services, and autonomous agents using (TypeScript/Node.js, Go, or Python, alongside frameworks like LangChain or CrewAI) to orchestrate complex provisioning, predictive auto-scaling, and closed-loop self-healing systems
AI-Driven Cloud Governance & Economics: Leverage predictive machine learning models to analyze multi-cloud spend patterns, autonomously executing real-time resource-optimization strategies via API-driven software actions (e.g., dynamic spot-instance bidding, intelligent right-sizing across AWS, Azure, and GCP)
Cognitive Observability & Infrastructure Security: Implement next-gen observability frameworks (OpenTelemetry, Prometheus) coupled with AI anomaly detection
Embed security directly into the deployment pipeline, utilizing LLMs to automatically audit Cloud IAM policies, scan for vulnerabilities, and generate contextual patches
Optimize resource allocation, cluster auto-scaling, and service meshes using AI-driven traffic routing and predictive capacity planning
Autonomous Incident Response: Act as a tier-3 software escalation engineer for complex distributed systems anomalies
Help design and train our internal 'On-Call AI Agent' to ingest logs, perform automated Root Cause Analysis (RCA), and submit pre-validated Pull Requests to resolve underlying system defects
Requirements
6+ years of experience in Cloud Software Engineering, Site Reliability Engineering (SRE), or Distributed Systems Infrastructure
2+ years of hands-on experience integrating AI tools, LLMs, or predictive analytics into deployment workflows, pipelines, or software platforms
Proven track record of architecting and operating large-scale, high-throughput distributed systems
Strong software engineering fundamentals in TypeScript (Node.js), Go, or Python
Experience interfacing with LLM APIs (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock), vector databases, and prompt engineering for systems-level orchestration
Deep proficiency in at least two major cloud platforms (AWS, Azure, GCP) with a strong architectural understanding of the third
Expert-level knowledge of Kubernetes (CKA preferred) and cloud-native networking
Experience building intelligent delivery pipelines using GitHub Actions or GitLab CI, featuring integrated automated testing, security gates, and AI-assisted code reviews
Deep understanding of Linux internals, distributed systems architecture, asynchronous programming patterns, and performance tuning
Nice to have
Agentic Problem-Solving: A mindset that moves past 'how do I automate this task?' to 'how do I build an autonomous system that solves this permanently?'
Collaborative AI-First Culture: Ability to partner with Core AI/ML teams to bridge the gap between model deployment and high-availability cloud infrastructure