Remote LLM Agentic AI Lead Architect & Deploy Agents

Steneral Consulting; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Steneral Consulting is seeking an LLM Agentic AI Lead to design, build, and deploy AI agents that solve business problems. You will guide a team from prototype to production, shaping the technical direction for agentic AI solutions.

The ideal candidate brings hands-on experience with LLMs, RAG, tool calling, and agent orchestration, and can communicate decisions clearly to business and engineering teams.

Qualifications

• 3+ years of experience with Python, ML, and AWS.
• Hands-on experience developing LLM applications and RAG solutions.
• Experience building agentic workflows with LangChain, LangGraph, or similar tools.
• Experience integrating LLMs with APIs, tools, enterprise systems, and data sources.

Responsibilities

• Lead the architecture and development of agentic AI solutions using LLMs, RAG, tools, and APIs.
• Design agent workflows that can retrieve information, reason through tasks, take actions, and return results.
• Guide decisions on model selection, prompting, embeddings, vector databases, and retrieval pipelines.
• Establish approaches for evaluating agent performance (response quality, latency, cost).
• Build safeguards such as access controls, human review for sensitive actions, and monitoring for production agents.
• Partner with stakeholders to turn business needs into practical AI use cases and delivery plans.
• Mentor engineers and communicate designs through diagrams and technical docs.

Skills

Python

Machine learning

AWS

LLM applications

RAG

LangChain

API integration

Embeddings

Prompt engineering

Production monitoring

Technical leadership

Tools

LangChain

LangGraph

AWS Bedrock

Docker

Kubernetes

ArgoCD

Job description

Steneral Consulting is seeking an LLM Agentic AI Lead to design, build, and deploy AI agents that solve business problems. You will guide a team from prototype to production, shaping the technical direction for agentic AI solutions.

The ideal candidate brings hands-on experience with LLMs, RAG, tool calling, and agent orchestration, and can communicate decisions clearly to business and engineering teams.