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.
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
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
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
Define and execute test strategies for AI systems
Measure system performance (latency, throughput, accuracy, cost)
Debug and optimize production systems.

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)

AI Native Software Engineer

Key skills