AI Developer, Quality Engineer

MarkJames Search

Charlotte (NC)

Hybrid

USD 96,432 - 110,208

Full time

14 days+

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Job summary

A leading recruitment firm is seeking an AI Developer for a 2-year contract to enhance Quality Engineering through innovative AI solutions. The ideal candidate will design and deploy agents for decision-making and automation, requiring at least 3 years of experience in AI/ML development with proficiency in Python and machine learning frameworks. This position offers competitive pay and the option to work in New York City, Charlotte, or Dallas.

Qualifications

At least 3 years of experience in AI/ML/NLP development.
Hands-on experience building LLM-based applications.
Familiarity with quality engineering concepts.

Responsibilities

Design and implement AI-driven agents that support the full quality engineering lifecycle.
Fine-tune and deploy LLMs and RAG pipelines for QE tasks.
Integrate AI agents into existing enterprise CI/CD pipelines.

Skills

Python

TensorFlow

PyTorch

Hugging Face Transformers

Machine Learning

Natural Language Processing

Education

Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or related field

Tools

Docker

Git

AWS

Azure

GCP

RESTful APIs

Job description

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This range is provided by MarkJames Search. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$70.00/hr - $80.00/hr

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Talent & Operations Specialist | Recruitment Strategy | Multilingual

AI Developer, Quality Engineering

Location: New York City, Charlotte NC, or Dallas TX

Duration: 2-year contract

We're looking for an AI Developer to join our client's team for a 2-year contract. In this role, you'll be instrumental in designing and deploying cutting-edge agentic AI solutions to automate and enhance Quality Engineering (QE) processes. You will work closely with QE architects, product managers, and data scientists to create and implement a variety of autonomous and assistive agents.

This position is open to candidates who can work from our client's offices in New York City, Charlotte, North Carolina, or Dallas, Texas.

About the Role

As an AI Developer, you will be responsible for building innovative agentic AI solutions that augment Quality Engineering. These solutions will include:

Knowledge Agents for intelligent decision-making and contextual data retrieval.
Test Case and Script Generation Agents that use LLMs and domain ontologies to create executable automation directly from user stories.
Defect Triage Agents that analyze logs, cluster similar bugs, and propose fixes.
Risk-based Prioritization Agents that intelligently select and plan tests.

Key Responsibilities

Design and implement AI-driven agents that support the full quality engineering lifecycle.
Fine-tune and deploy LLMs and retrieval-augmented generation (RAG) pipelines for specific QE tasks.
Utilize multi-modal and multi-agent systems for code, test, and defect generation.
Work with QE teams to understand their needs and optimize agent performance.
Integrate AI agents into existing enterprise CI/CD pipelines and DevOps toolchains.
Continuously improve agent capabilities using feedback loops and other learning strategies.
Evaluate and benchmark the performance of AI agents using key QE metrics.

Qualifications

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or a related field.
At least 3 years of experience in AI/ML/NLP development, with a strong preference for proficiency in Python, TensorFlow, PyTorch, or Hugging Face Transformers.
Hands-on experience building LLM-based applications, including prompt engineering, fine-tuning, and RAG.
Familiarity with quality engineering concepts, such as test design, automation frameworks (e.g., Selenium, Cypress), and the defect lifecycle.
Experience with vector databases (e.g., FAISS, Weaviate, Pinecone) and embeddings.
Proficiency with RESTful APIs, Docker, Git, and major cloud platforms (AWS, Azure, or GCP).

Preferred Qualifications

Experience with autonomous agents or agent frameworks like LangChain, AutoGen, or CrewAI.
Knowledge of software testing tools (e.g., JIRA, TestRail, Postman).
Understanding of DevOps pipelines and tools (e.g., Jenkins, GitHub Actions).
Exposure to MLOps practices for continuous learning and model deployment.
Background in reinforcement learning or graph-based reasoning.

Seniority level