AI Engineer
Role details
Job location
Tech stack
Job description
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
Requirements
- 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.
- Retrieval-Augmented Generation (RAG) and Large Language Model (LLM) Generative AI
- Coding is mandatory, 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