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Remote Ai Validation Jobs (NOW HIRING)

MRINetwork Jobs

Domain Expert

Independence, KS · Remote

... AI Validation, Prompt Engineering, Data Analysis, Research, Content Review, Technical Writing, Critical Thinking, Analytical Skills, Remote Jobs, Work From Home, Contract Position, Technology ...

MRINetwork Jobs

Domain Expert

Independence, KS · Remote

... AI Validation, Prompt Engineering, Data Analysis, Research, Content Review, Technical Writing, Critical Thinking, Analytical Skills, Remote Jobs, Work From Home, Contract Position, Technology ...

MRINetwork Jobs

Domain Expert

Independence, KS · Remote

... AI Validation, Prompt Engineering, Data Analysis, Research, Content Review, Technical Writing, Critical Thinking, Analytical Skills, Remote Jobs, Work From Home, Contract Position, Technology ...

MRINetwork Jobs

Domain Expert

Independence, KS · Remote

... AI Validation, Prompt Engineering, Data Analysis, Research, Content Review, Technical Writing, Critical Thinking, Analytical Skills, Remote Jobs, Work From Home, Contract Position, Technology ...

MRINetwork Jobs

Domain Expert

Independence, KS · Remote

... AI Validation, Prompt Engineering, Data Analysis, Research, Content Review, Technical Writing, Critical Thinking, Analytical Skills, Remote Jobs, Work From Home, Contract Position, Technology ...

MRINetwork Jobs

Domain Expert

Independence, KS · Remote

... AI Validation, Prompt Engineering, Data Analysis, Research, Content Review, Technical Writing, Critical Thinking, Analytical Skills, Remote Jobs, Work From Home, Contract Position, Technology ...

TEKsystems

AI Engineer

San Diego, CA · Remote

$50 - $58/hr

Remote AI/ML Software Engineer, Generative AI Are you passionate about building Generative AI ... Work with engineering, quality, validation, and business teams to deliver practical AI solutions.

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Remote Ai Validation information

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$78

How much do remote ai validation jobs pay per hour?

As of Sep 2, 2026, the average hourly pay for remote ai validation in the United States is $52.00, according to ZipRecruiter salary data. Most workers in this role earn between $39.42 and $63.22 per hour, depending on experience, location, and employer.

What is a remote AI validation?

A Remote AI Validation job involves evaluating and testing artificial intelligence models to ensure they work accurately and reliably. People in this role often review AI-generated content, annotate data, or provide feedback on machine learning outputs. The work is typically done online, allowing for flexible, remote schedules. Remote AI Validators play a crucial role in improving AI systems by identifying errors, biases, or inaccuracies in model predictions.

What are the key skills and qualifications needed to thrive as a remote AI validation specialist?

To thrive as a Remote AI Validation Specialist, you need strong analytical abilities, attention to detail, and a background in computer science, data science, or a related field. Familiarity with machine learning frameworks, data annotation tools, and quality assurance platforms is typically required. Excellent communication, problem-solving skills, and the ability to work independently are essential soft skills for success in a remote environment. These skills ensure accurate validation of AI models, high-quality data outputs, and effective collaboration with distributed teams.

What are the typical challenges faced by professionals working in remote AI validation roles, and how can they be addressed?

Professionals in remote AI validation roles often encounter challenges such as managing communication across distributed teams, ensuring consistent access to data and computational resources, and maintaining alignment on validation protocols. Overcoming these hurdles typically involves leveraging collaborative tools, establishing clear documentation practices, and participating in regular virtual meetings. Additionally, staying updated with evolving AI validation standards and fostering open communication with data scientists, engineers, and product managers can help ensure accuracy and efficiency in the validation process.

What is the difference between Remote Ai Validation vs Remote Data Labeler?

AspectRemote Ai ValidationRemote Data Labeler
Required CredentialsBasic understanding of AI/ML concepts, sometimes with certificationsNo formal credentials typically required
Work EnvironmentRemote, often collaborative with AI teamsRemote, individual or team-based labeling tasks
Industry UsageUsed in AI development, quality assurance for modelsUsed in data preparation for machine learning
Common Search IntentComparing roles in AI validation and data labelingLooking for data annotation or labeling jobs

Remote Ai Validation involves verifying and ensuring the quality of AI outputs, often requiring some understanding of AI/ML concepts. Remote Data Labeler focuses on annotating data for training models, typically with minimal formal credentials. Both roles are remote and essential in AI development, but they differ in responsibilities and skill requirements.

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What states have the most Remote Ai Validation jobs?

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Remote Ai Validation jobs near you
Infographic showing various Remote Ai Validation job openings in the United States as of August 2026, with employment types broken down into 33% Internship, and 67% Full Time. Highlights an 100% Remote job distribution, with an average salary of $108,152 per year, or $52 per hour.

Agentic AI Engineering Architect (Remote -US)

OMG Technology

Jersey City, NJ • Remote

Contractor

This job post has expired today. Applications are no longer accepted.

Job description

Agentic AI Engineering Architect – Remote (US)
We are looking to hire a candidate with the mentioned skill sets and experience for one of our clients within the pharmaceutical Industry. This is a REMOTE role.

Position Overview

Lead the transformation of the software engineering organization into an AI-augmented, agentic engineering model. You will architect an enterprise framework that uses AI agents across the Product Development Life Cycle (PDLC) from requirements and architecture through development, testing, DevSecOps, release, and operations.

Key Responsibilities

  • Define the AI-native SDLC / Agentic PDLC architecture and roadmap.
  • Architect multi-agent workflows for requirements, architecture, development, testing, security, DevOps, compliance, and release management.
  • Design the enterprise AI Engineering Platform/Harness, including LLM gateway, RAG, vector databases, knowledge graphs, MCP, agent registry, orchestration, memory, observability, and evaluation.
  • Drive AI-enabled software engineering transformation, including AI coding, code review, automated testing, documentation, refactoring, and DevSecOps.
  • Define enterprise architectures across cloud, microservices, APIs, data, event-driven systems, and AI infrastructure.
  • Establish AI governance, security, Responsible AI, validation, and regulatory compliance practices.
  • Lead enterprise AI adoption, engineering enablement, training, and change management.
  • Evaluate and select AI platforms and frameworks such as OpenAI, Azure AI, AWS Bedrock, Google Gemini, Anthropic, LangGraph, AutoGen, Semantic Kernel, LangChain, MCP, GitHub Copilot, Cursor, and Claude Code.

Required Qualifications

  • Bachelor’s degree in Computer Science, Engineering, or related field; Master’s preferred.
  • 12+ years of enterprise software engineering experience.
  • 8+ years leading enterprise architecture initiatives.
  • 5+ years leading cloud-native engineering transformations.
  • Experience leading large Agile engineering organizations (200+ engineers preferred).
  • Strong experience in enterprise software modernization and DevSecOps at scale.
  • Strong understanding of Generative AI, Agentic AI, AI engineering platforms, and enterprise AI adoption.
  • Experience implementing GenAI solutions in enterprise environments.
  • Strong enterprise architecture, technical leadership, and organizational transformation skills.

Key Technical Skills

AI/Agentic AI: GenAI, LLMs, Agentic AI, RAG, Knowledge Graphs, LLMOps, AI Evaluation, AI Safety, MCP, Context Engineering.
Engineering: Python, Java, C#, TypeScript, Node.js, React, REST/GraphQL, Microservices, Kubernetes, Docker, GitHub, Azure DevOps, Terraform, CI/CD.
Cloud/Data: Azure, AWS, GCP, Databricks, Snowflake, PostgreSQL, Redis, Vector Databases, Neo4j.
AI Frameworks: LangGraph, CrewAI, AutoGen, Semantic Kernel, PydanticAI, LangChain, LlamaIndex, OpenAI/Anthropic SDKs, Azure AI Foundry.

Other Job Details:

  • Job Type: C2C or W2. (Open to market rate)
  • Duration: 6+ months with a high possibility of extension.
  • Location: REMOTE.
  • Interviews: Video interviews.
  • Docs required: ID proof will be required.