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

Requirements

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)

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonJavaAI agentsRetrieval (RAG)Microservices architectureContainersServerlessCI/CD pipelinesInfrastructure as codeMonitoring and observability