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.
What You Bring- 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
Preferred Experience- 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
What You'll Do- 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
Your first few weeks at Rearc will be spent in an immersive learning environment where our team will help you get up to speed. Within the first few months, you'll have the opportunity to experiment with a lot of different tools as you find your place on the team.
