Python AI Engineer

Job Title: Python AI Engineer
Location: Charlotte, NC or Dallas, TX
Work Mode: Onsite 5 Days/Week
Rate: $65 $70/hr
Employment Type: Contract for 6 months ( with extension)

Role Overview

We are seeking an experienced Python AI Engineer to design and build advanced AI-driven applications leveraging large language models (LLMs) and modern generative AI techniques. This role focuses on developing intelligent systems using foundation models, agentic frameworks, and knowledge-driven architectures such as GraphRAG.

The ideal candidate will combine strong AI/ML engineering expertise with full-stack development capabilities to build scalable applications, integrate AI services, and deploy solutions in cloud environments.

Key Responsibilities

• Build applications leveraging foundation models and advanced Generative AI techniques, including GraphRAG.

• Develop autonomous AI agents using modern agentic frameworks.

• Implement Model Context Protocol (MCP) integrations and Python-based full-stack solutions.

• Design and develop scalable REST APIs for AI-powered applications.

• Deploy AI/ML workloads across major cloud platforms such as AWS, Azure, or Google Cloud Platform.

• Apply LLMOps and MLOps best practices, including CI/CD pipelines, monitoring, and model lifecycle management.

• Work cross-functionally with engineering and product teams to deliver innovative AI solutions.

• Mentor team members and contribute to innovation in embeddings, knowledge graphs, and ontology-based systems.

Required Qualifications

• Bachelor s degree in Computer Science, AI/ML, or a related field.

• 5+ years of experience in AI/ML-focused software engineering.

• Production experience working with Large Language Models (LLMs) and agentic systems.

• Strong expertise in Python and AI/ML libraries.

• Experience working with embeddings at scale, knowledge graphs, ontology extraction, and GraphRAG architectures.

• Full-stack development experience (Python backend with modern frontend technologies).

• Experience deploying applications on cloud platforms such as AWS, Azure, or Google Cloud Platform.

• Familiarity with LLMOps tools and frameworks.

• Strong problem-solving, communication, and collaboration skills.