Seeking a hands-on AI Native Software Engineer to design, build, and deploy production-grade AI-driven systems within enterprise environments. The role focuses on implementing agent-based workflows, integrating AI platforms, and delivering scalable cloud-native solutions. Responsibilities AI Agent Engineering • Design and implement AI agents, including: Retrieval (RAG) Orchestration workflows Tool/function invocation Policy-based routing • Build evaluation frameworks for accuracy, latency, and reliability • Implement observability and monitoring for agent lifecycle. AI Platform Integration • Integrate with AI providers (e.g., OpenAI, Anthropic, Google Vertex, open-source models) • Build abstraction layers to support multi-model and multi-provider architectures • Optimize model usage for performance, cost, and latency Cloud-Native Development • Develop scalable services using: Microservices architecture Containers (Docker, Kubernetes) Serverless and event-driven patterns • Implement CI/CD pipelines and infrastructure as code (e.g., Terraform, Helm) • Ensure production readiness, logging, monitoring, and fault tolerance Application Development • Build and deploy AI-powered applications aligned to business workflows • Integrate AI systems into existing enterprise platforms and APIs • Develop backend services and APIs supporting agent workflows Testing & Performance • Define and execute test strategies for AI systems • Measure system performance (latency, throughput, accuracy, cost) • Debug and optimize production systems. Required Skills & Experience • 8–10+ years of software engineering experience • Strong experience with cloud-native systems (APIs, microservices, containers, serverless) • Experience building and deploying AI/LLM-based systems in production (agents, RAG, orchestration) • Proficiency in Python, Java, or similar backend languages • Experience with: CI/CD pipelines Infrastructure as code Monitoring and observability tools • Hands-on experience with AI platforms (OpenAI, Claude, Vertex AI, or similar) • Experience with agent frameworks (e.g., LangGraph, AutoGen, CrewAI) • Experience designing multi-agent or distributed AI systems • Familiarity with enterprise-scale system integration • Experience optimizing AI workloads for cost and performance. • Scope & Expectations • 100% hands-on engineering role (no people management) • Deliver production-quality code and deployments • Work within existing architecture and engineering standards • Collaborate with client and internal engineering teams as needed • Participate in technical design discussions (implementation-focused)