Artificial Intelligence Engineer

INA Solution; Columbus (OH); On-site; USD 120,000 - 160,000; Full time

Job summary

INA Solution in Columbus, OH seeks a hands-on AI Engineer to design and deploy enterprise-grade AI applications powered by LLMs and intelligent agents. The role emphasizes Python, ML, RAG pipelines, and agent orchestration to deliver production-ready solutions.

You'll collaborate across teams to integrate AI with enterprise data platforms, evaluate models for performance and cost, and implement guardrails for security and privacy. This is a hands-on, impact-driven position.

Qualifications

• Experience designing and deploying enterprise AI solutions.
• Hands-on Python and ML development with focus on production-ready systems.
• Experience building RAG pipelines and integration with enterprise data sources.

Responsibilities

• Build and deploy LLM-powered applications, copilots, and AI agents.
• Develop RAG pipelines using embeddings, vector databases, and retrieval techniques.
• Implement agent workflows using LangChain, LangGraph, or Agents SDK.
• Integrate AI solutions with enterprise applications, APIs, and data platforms.
• Evaluate and optimize models for accuracy, performance, cost, and reliability.
• Implement monitoring, testing, security, privacy, and responsible AI guardrails.
• Collaborate with cross-functional teams to deliver scalable, production-ready AI solutions.

Skills

Python

Machine learning

RAG pipelines

Agent orchestration

Cloud AI services

Model evaluation

AI security

Data privacy

Tools

LangChain

LangGraph

Agents SDK

Vector databases

Embeddings

APIs

Job description

We are seeking a hands-on AI Engineer to design, develop, and deploy enterprise-grade AI applications powered by LLMs and intelligent agents. The ideal candidate will have strong Python and machine learning skills, with experience in RAG pipelines, agent orchestration, cloud AI services, model evaluation, and AI security.

Key Responsibilities

• Build and deploy LLM-powered applications, copilots, and AI agents.
• Develop RAG pipelines using enterprise data, embeddings, vector databases, and retrieval techniques.
• Implement agent workflows using LangChain, LangGraph, or Agents SDK.
• Integrate AI solutions with enterprise applications, APIs, and data platforms.
• Evaluate and optimize models for accuracy, performance, cost, and reliability.
• Implement monitoring, testing, security, privacy, and responsible AI guardrails.
• Collaborate with cross-functional teams to deliver scalable, production-ready AI solutions.