Agentic AI Engineer

Agentic AI Engineer
Experience: 7+ Years
About the Role
We are looking for a hands-on Agentic AI Engineer to build core technical foundations and production-grade AI solutions. In this role, you will design scalable agentic architectures, build evaluation frameworks, develop reusable accelerators, and ship enterprise-grade backend services.
This position offers a strong growth pathway toward Forward Deployed Engineering (FDE) for engineers who want to develop business acumen and progressively take on customer-facing solution architecture responsibilities.
What You’ll Do
Design & Deploy Production AI: Build production-ready Agentic AI systems (RAG, multi-step orchestration, tool/function calling) that move from POC to customer adoption.
Core Engineering & Accelerators: Develop reusable components, backend APIs, and evaluation frameworks (quality gates, offline/online eval harnesses) to accelerate future deployments.
Operationalize for Scale: Tune systems for economics and performance (latency, cost, throughput) while implementing reliability patterns (retries, fallbacks, idempotency).
Durable Handoffs: Ensure clean transitions to operational teams with documentation, telemetry, and support runbooks.
Key Requirements
7+ years in software, backend, or AI engineering roles with a strong record of shipping production-quality systems.
Hands-on LLM Experience: Proven track record with RAG, tool calling, workflow orchestration, and evaluation/quality frameworks.
Robust Backend & Integration Skills: Expertise in APIs, system architecture, data integrations, and enterprise patterns.
Production Operations: Hands-on experience with cloud infrastructure (AWS/Azure/GCP), CI/CD, containers, security (PII, access control), and LLM telemetry/monitoring.
Exposure to customer environments is a plus, along with an interest in progressing toward customer-facing FDE roles.
Primary Skills
Backend/Full-Stack Engineering | LLM Orchestration & RAG | System Architecture | AI Quality & Eval Harnesses | Cloud & CI/CD | Telemetry & Observability

Work arrangement
No