Remote AI Engineer: LLM & Automation Architect

TeamLogicIT; United States; Remote; USD 150,000 - 190,000; Full time

Benefits offered by this job

Industry-leading compensation

HR benefits

Job summary

TeamLogicIT is seeking an AI Engineer to design, build, and deploy production-grade LLM solutions on Azure, focusing on RAG pipelines and agentic AI systems. You will architect multi-agent workflows using LangChain, LangGraph, CrewAI, AutoGen, and LlamaIndex, and integrate AI with enterprise systems via REST APIs and webhooks.

You will lead MLOps, telemetry-driven improvements, and user adoption, while mentoring junior engineers and presenting to clients and stakeholders across industries.

Qualifications

• 2+ sentences: 4–6 years hands-on Generative AI / LLM solution deployment experience.
• Proficiency in Python for AI development, API integration, and backend services (FastAPI, REST).
• Experience with multi-agent/workflow frameworks and RAG pipelines.

Responsibilities

• Design, build, and deploy production-grade LLM solutions on Azure.
• Architect multi-agent workflows using LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex.
• Integrate AI with enterprise systems via REST APIs and webhooks (Jira, ServiceNow, CRMs, ERPs).
• Lead MLOps, evaluation pipelines, and telemetry-driven improvements.
• Mentor and enable teams; present to clients and stakeholders.

Skills

Python

LLM frameworks

Azure OpenAI

REST APIs

LangChain

LangGraph

AutoGen

CrewAI

LlamaIndex

API integration

English communication

Secure design

Tools

LangChain

LangGraph

AutoGen

CrewAI

LlamaIndex

Azure OpenAI Service

Azure AI Search

Azure Data Factory

Jira

ServiceNow API

Job description

TeamLogicIT is seeking an AI Engineer to design, build, and deploy production-grade LLM solutions on Azure, focusing on RAG pipelines and agentic AI systems. You will architect multi-agent workflows using LangChain, LangGraph, CrewAI, AutoGen, and LlamaIndex, and integrate AI with enterprise systems via REST APIs and webhooks.

You will lead MLOps, telemetry-driven improvements, and user adoption, while mentoring junior engineers and presenting to clients and stakeholders across industries.