AI Native Software Engineer

Job Summary

Rearc is seeking a hands-on AI Native Software Engineer to design, build, and deploy production-grade AI-driven systems within enterprise environments. This fully remote role focuses on building production systems, agent-based workflows, integrating AI platforms, and delivering scalable, cloud-native solutions. You will 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. As part of a company founded in 2016, you will join a community of problem-solvers dedicated to empowering engineers and fostering creativity without bureaucracy.

Key Responsibilities

• Design and implement AI agents, including RAG pipelines, orchestration workflows, and tool invocation patterns.
• 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 in terms of latency, throughput, accuracy, and cost.
• Debug and optimise production systems.
• Collaborate with client and internal engineering teams.
• Participate in technical design discussions, focused on implementation.

Role Requirements & Qualifications

• 8 to 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, such as RAG, agents, or orchestration workflows.
• Hands-on experience with AI platforms like OpenAI, Anthropic, Google Vertex, or similar.
• Experience designing and implementing retrieval systems (RAG), agent workflows and orchestration, and tool/function invocation patterns.
• Strong understanding of system-level trade-offs including performance, cost, latency, and 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.
• No specific degree requirement, but a degree in Computer Science or related field is a plus.

Skills Inventory

Essential Skills:

• Backend development in Python, Java, or similar languages.
• Cloud-native architecture (AWS, GCP, or Azure).
• Containerisation (Docker, Kubernetes) and serverless computing.
• AI/LLM integration with providers like OpenAI, Anthropic, or Vertex AI.
• RAG pipeline design and implementation.
• Agent orchestration frameworks.
• CI/CD and infrastructure as code (Terraform, CloudFormation).
• Observability and monitoring tools (Prometheus, Grafana, OpenTelemetry).
• System performance optimisation.

Preferred Skills:

• Experience with agent frameworks such as LangGraph, AutoGen, or CrewAI.
• Experience designing multi-agent or distributed AI systems.
• Familiarity with enterprise-scale system integration.
• Experience optimising AI workloads for cost and performance.