Agentic AI Engineer

What you'll do

• Design and deploy agent-to-agent communication architectures for autonomous task delegation and multi-step business processes.
• Integrate proprietary and open-source LLMs with enterprise APIs and third-party SaaS applications using tool-calling methods.
• Implement Retrieval-Augmented Generation (RAG) techniques using search and vector storage engines to ground agent operations.
• Provision and manage scalable cloud infrastructure on AWS, Azure, or GCP using Terraform.
• Create CI/CD pipelines for automated testing, deployment, and versioning of agentic workflows.
• Apply security fundamentals including VPC configurations, secure API gateways, and IAM controls to protect inter-agent communications.
• Build observability systems to monitor multi-agent interactions, model accuracy, token spending, and agent loops.
• Maintain detailed technical documentation for LLM integration protocols and cloud infrastructure deployments.

What we're looking for

• Deep experience integrating, fine-tuning, and optimizing foundation models including prompt engineering and tool calling.
• Proven success building multi-agent systems, inter-agent messaging pipelines, and dynamic task delegation protocols.
• Hands-on experience with multi-agent orchestration frameworks such as LangGraph, LangChain, or AutoGen.
• Advanced proficiency in Python or TypeScript for backend orchestration and API service development.
• Practical knowledge of RAG patterns, vector databases, and hybrid search architectures.
• Hands-on experience with DevOps practices and infrastructure automation using Terraform, Docker, and Kubernetes.
• Strong knowledge of secure cloud architectures, zero-trust network design, and identity management (OAuth, SAML, IAM).
• Experience setting up observability frameworks to track non-deterministic multi-agent workflows and token costs.