Lead AI Engineer | Cloud-Native & Agentic AI

Brillio-2; United States; Hybrid; USD 27,000 - 43,000; Full time

Job summary

Brillio-2 is seeking a Lead AI Engineer I to design and build end-to-end intelligent applications with cloud-native architectures. You will develop scalable backend systems, leverage multi-agent workflows, and work with LLM frameworks to orchestrate autonomous processes.

The role requires 4–6 years in ML engineering, strong Python and JS/TS, and hands-on cloud deployment across Azure, AWS, or GCP, with experience in CI/CD and observability.

Qualifications

• 4–6 years in ML engineering with cloud-native architectures.
• Hands-on with agentic AI frameworks and scalable full-stack systems.
• Strong Python and JS/TS, plus modern frontend/backend tech.
• Experience deploying in Azure, AWS, or GCP.
• Familiarity with multi-agent systems and agent orchestration.

Responsibilities

• Design and build end-to-end full-stack intelligent apps.
• Develop and deploy cloud-native solutions on major cloud platforms.
• Build agentic AI apps including multi-agent workflows.
• Develop scalable backends with microservices and event-driven design.
• Work with LLM frameworks and RAG-based workflows.
• Maintain code quality, tests, observability, and performance.
• Collaborate across teams on architecture decisions.

Skills

Python

JavaScript/TypeScript

Frontend frameworks

Backend tech

Cloud platforms

Docker

Kubernetes basics

REST APIs

Agentic AI frameworks

Prompt engineering

RAG

CI/CD

Education

Bachelor's degree

Tools

LangChain

LlamaIndex

Semantic Kernel

AutoGen

CrewAI

Job description

Brillio-2 is seeking a Lead AI Engineer I to design and build end-to-end intelligent applications with cloud-native architectures. You will develop scalable backend systems, leverage multi-agent workflows, and work with LLM frameworks to orchestrate autonomous processes.

The role requires 4–6 years in ML engineering, strong Python and JS/TS, and hands-on cloud deployment across Azure, AWS, or GCP, with experience in CI/CD and observability.