Agentic AI Software Engineer
About The Position
We are seeking an experienced Agentic AI Software Engineer to design and implement AI agents, including retrieval (RAG), orchestration workflows, tool/function invocation, and policy-based routing. You will also build evaluation frameworks for accuracy, latency, and reliability, and implement observability and monitoring for the agent lifecycle. This role involves integrating with AI providers (e.g., OpenAI, Anthropic, Google Vertex, open-source models) and building abstraction layers to support multi-model and multi-provider architectures. You will also optimize model usage for performance, cost, and latency. Additionally, you will develop scalable services using microservices architecture, containers (Docker, Kubernetes), and serverless/event-driven patterns, implementing CI/CD pipelines and infrastructure as code (e.g., Terraform, Helm). Ensuring production readiness, logging, monitoring, and fault tolerance is crucial. You will build and deploy AI-powered applications aligned with business workflows, integrate AI systems into existing enterprise platforms and APIs, and develop backend services and APIs supporting agent workflows. Defining and executing test strategies for AI systems, measuring system performance (latency, throughput, accuracy, cost), and debugging/optimizing production systems are key responsibilities.
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
- Experience with Infrastructure as Code
- Experience with Monitoring and observability tools
- Hands-on experience with AI platforms (OpenAI, Claude, Vertex AI, or similar)
Nice To Haves
- Experience with agent frameworks (e.g., LangGraph, AutoGen, CrewAI)
- Experience designing multi-agent or distributed AI systems
- Familiarity with enterprise-scale system integration
- Experience optimizing AI workloads for cost and performance
Responsibilities
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
