Azure OpenAI Developer, Code Review & Evaluation

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We help people start and grow careers teaching AI — discover projects, build a profile, and apply quickly. OpenTrain is the hiring organization for this role.

• Work on cutting-edge tasks that directly shape how AI models behave.
• Flexible, remote-friendly roles that fit around other commitments.

About AI Training Work

AI training (also called data labeling or human feedback work) is the human side of building AI. Contributors create, review, and rate examples that modern models learn from — including annotating code, evaluating model responses, and improving deployment guidance.

This role focuses on evaluating AI-generated technical content about Azure OpenAI deployments: accuracy, best practices, and developer guidance.

• Typical tasks include marking correctness, suggesting fixes, and explaining why an output is right or wrong.
• Many projects require clear written feedback in English and solid domain knowledge rather than formal credentials.

The Role

We’re hiring an experienced Azure OpenAI developer to analyze and label AI-generated prompts and responses relating to Azure OpenAI Service, model deployment, authentication, and API usage. Your feedback will be used to improve the AI’s guidance for developers working in Azure.

This is a part-time contractor role (under 20 hours/week), remote and worldwide, paid $20 USD per hour.

• Position type: Contractor, Part-time.
• Hours: Less than 20 hours/week (flexible scheduling).
• Pay: $20 USD per hour.
• Worldwide applicants accepted; strong English writing skills required.

What You’ll Do

Your core responsibility is to evaluate AI-generated code snippets, explanations, and deployment recommendations for correctness, security, performance, and adherence to Azure best practices. You will label outputs, categorize issues, and provide structured, actionable feedback.

A second key responsibility is conducting structured technical evaluations (AI-driven interviews) of candidates applying as Azure OpenAI developers according to provided guidelines.

• Label and categorize AI-generated responses for accuracy, relevance, and adherence to Azure OpenAI API usage.
• Identify authentication, deployment, or configuration errors and recommend fixes.
• Assess efficiency and cost-optimization suggestions for API calls and token usage.
• Produce clear, structured feedback in English that developers can act on.
• Run technical interview tasks to verify hands-on Azure OpenAI experience and debugging ability.

Requirements

Please preserve the following mandatory requirements when applying and during evaluations. These come from the project brief and must not be omitted.

Note: the role explicitly requires hands-on, real-world experience and clear English communication.

• 5+ years hands-on experience with Azure OpenAI Service, model deployment, and API integration.
• Experience with Azure-based authentication (Azure Active Directory, Managed Identities) for API calls.
• Proven ability to manage AI workloads on cloud infrastructure and optimize API calls for enterprise use.
• Strong English writing skills — you will produce structured feedback and run candidate interviews.
• Comfort debugging code snippets and explaining fixes clearly and concisely.

Interview & Evaluation Process (AI-Driven)

Part of this role is to conduct or support AI-driven technical interviews that vet other candidates. Follow the structured guidelines below when assessing applicants or AI outputs.

If you are evaluating AI-generated assertions or candidate answers, probe for specific projects, real-world challenges, and concrete fixes — do not accept purely theoretical answers.

• Experience assessment: Ask applicants to describe projects deploying GPT/Codex/DALL·E on Azure, integration with Azure Functions or Cognitive Services, and specific challenges they solved.
• Technical knowledge check: Present buggy Azure OpenAI API code (example below) and ask the candidate to identify and fix issues and explain Azure AD authentication and scaling strategies.
• Example snippet to use in tests: import openai openai.api_base = "https://your-azure-openai-endpoint.com/" openai.api_key = "your-api-key" response = openai.ChatCompletion.create( model="gpt-4", messages=[{"role": "user", "content": "Hello, world!"}] ) print(response)
• AI evaluation tasks: Ask candidates to correct statements such as: "Azure OpenAI Service allows you to train and fine-tune models using built-in AutoML capabilities." — expect a clear correction and rationale.
• Communication & clarity: Require candidates to explain differences between Azure OpenAI and OpenAI’s public API in simple terms and to describe a real-world enterprise application.
• Final checks: Confirm prior experience evaluating AI-generated code, availability for labeling tasks, and attention to detail. Reject candidates who provide only theoretical knowledge or cannot debug real-world examples.

How To Apply & Next Steps

Apply with a short summary of your hands-on Azure OpenAI experience, links to any public projects or code samples (if available), and a brief example of a bug you fixed in an Azure OpenAI integration.

If selected you will perform a short technical evaluation (debugging + written feedback) so we can verify your hands-on skills and communication style before starting labeling tasks.

• Include: years of Azure OpenAI experience, examples of deployments, and familiarity with Azure AD or managed identities.
• Be prepared to inspect and correct sample code, and to provide structured feedback in English.
• OpenTrain will onboard you remotely and provide labeling guidelines and examples for the evaluation tasks.