Seeking a hands-on AI Native Software Engineer to design, build, and deploy production-grade AI-driven systems within enterprise environments.
This role focuses on building production systems, agent-based workflows, integrating AI platforms, and delivering scalable, cloud-native solutions. You’ll work across the full lifecycle — from system design through to production deployment — building AI-powered applications that integrate into real business workflows. This is a 100% hands-on engineering role, requiring strong software engineering fundamentals alongside practical experience with modern AI systems.
- 8–10+ years of software engineering experience
- Strong experience building cloud-native systems, including APIs, microservices, containers, and serverless architectures
- Proven experience building and deploying AI/LLM-based systems in production (e.g. RAG, agents, orchestration workflows)
- Hands-on experience with AI platforms (e.g. OpenAI, Anthropic, Google Vertex, or similar)
- Experience designing and implementing:
- Retrieval systems (RAG)
- Agent workflows and orchestration
- Tool/function invocation patterns
- Strong understanding of system-level trade-offs (performance, cost, latency, reliability)
- Experience with CI/CD pipelines, infrastructure as code, and production observability
- Proficiency in Python, Java, or similar backend languages
- Experience debugging and optimising production systems
- Experience with agent frameworks (e.g. LangGraph, AutoGen, CrewAI)
- Experience designing multi-agent or distributed AI systems
- Familiarity with enterprise-scale system integration
- Experience optimising AI workloads for cost and performance
- Design and implement AI agents, including RAG pipelines, orchestration workflows, and tool invocation
- Build evaluation frameworks to measure system accuracy, latency, and reliability
- Implement observability and monitoring across the AI system lifecycle
- Integrate with AI providers and build abstraction layers to support multi-model and multi-provider architectures
- Optimise AI systems for performance, cost, and scalability
- Develop cloud-native services using microservices, containers, and serverless patterns
- Build and deploy AI-powered applications aligned to business workflows
- Integrate AI systems into existing enterprise platforms and APIs
- Define and execute testing strategies for AI systems
- Measure and improve system performance (latency, throughput, accuracy, cost)
- Debug and optimise production systems
- Collaborate with client and internal engineering teams
- Participate in technical design discussions, focused on implementation
At Rearc , we're committed to empowering engineers to build awesome products and experiences. Success as a business hinges on our people's ability to thin