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Ai Tagging Jobs (NOW HIRING)

Next League, LLC

AI Solutions Engineer

New York, NY · On-site

$200/hr

Computer vision for content operations (highlight detection, automated tagging, content moderation, metadata enrichment) * Multi-modal AI systems that combine LLMs and vision models * Production ...

Eliassen Group

AI Software Engineer

Greenwood Village, CO · On-site +1

$50 - $56/hr

Eliminate manual bottlenecks across reporting, tagging, and testing workflows. * Build AI agents supporting requirements review, architecture, coding, testing, debugging, and deployment. * Capture ...

Eliassen Group

AI Software Engineer

Greenwood Village, CO · On-site +1

$50 - $56/hr

Eliminate manual bottlenecks across reporting, tagging, and testing workflows. * Build AI agents supporting requirements review, architecture, coding, testing, debugging, and deployment. * Capture ...

Next League, LLC

AI Solutions Engineer

New York, NY · On-site

$200/hr

Computer vision for content operations (highlight detection, automated tagging, content moderation, metadata enrichment) * Multi-modal AI systems that combine LLMs and vision models * Production ...

AUTEC

Lead Generation and AI Manager

Statesville, NC · On-site

Use AI tools to accelerate research, enrichment, tagging, and workflow execution * Build workflows using tools such as ChatGPT, Claude, Clay, Apollo, Zapier, Make, Airtable, or similar * Improve CRM ...

AUTEC

Lead Generation and AI Manager

Fort Worth, TX · Remote

Use AI tools to accelerate research, enrichment, tagging, and workflow execution * Build workflows using tools such as ChatGPT, Claude, Clay, Apollo, Zapier, Make, Airtable, or similar * Improve CRM ...

Ambrook

Software Engineer, AI

New York, NY · On-site +1

$135K - $220K/yr

Our internal tooling and infrastructure for classification, tagging, summarization, and user-in-the-loop AI systems. Within 1 month you'll... * Build and ship a feature powered by an LLM - e.g ...

QualX Corporation

AI Developer

Springfield, VA · On-site

Develop and integrate AI/ML solutions to enhance records management practices , including classification review, metadata tagging, digitization workflows, and compliance with DoD and Federal records ...

Purple Drive Technologies

AI Developer

Richardson, TX · On-site

Knowledge of automated speech recognition and SSML tagging for TTS * Certifications in IBM Watson or other conversational AI platforms * Experience with voice-enabled conversational interfaces

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Ai Tagging information

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$39K

$114.3K

$150K

How much do ai tagging jobs pay per year?

As of Jun 13, 2026, the average yearly pay for ai tagging in the United States is $114,320.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,500.00 and $134,500.00 per year, depending on experience, location, and employer.

What does a typical day look like for someone in an AI Tagging role?

A typical day for an AI Tagging professional includes reviewing and labeling large sets of data—such as images, audio, or text—according to specific guidelines provided by the project. You may spend time collaborating with data scientists or project managers to clarify labeling instructions or resolve ambiguous cases. Work is usually structured with clear quality and productivity targets, and you might also participate in feedback sessions to improve annotation consistency. The pace can be steady, with periods of high concentration, and you may use specialized software platforms to manage your workflow. Team communication and attention to detail are key aspects of the job each day.

What are the key skills and qualifications needed to thrive in the Ai Tagging position, and why are they important?

To thrive in AI Tagging, you need strong attention to detail, data annotation skills, and familiarity with data quality standards, often backed by experience or coursework in information science or computer science. Familiarity with data labeling platforms, image and text annotation tools, and occasionally basic programming or scripting knowledge is beneficial. Strong organizational skills, patience, and the ability to work efficiently both independently and within a team distinguish top performers in this role. These skills ensure the accurate and efficient creation of high-quality labeled datasets that are essential for training and improving AI models.

What is an AI Tagging job?

An AI Tagging job involves labeling or annotating data to help train machine learning models. This can include tagging images, videos, text, or audio with relevant metadata so that AI systems can recognize patterns and improve accuracy. AI taggers follow specific guidelines to ensure consistency and quality in the annotations. It's a crucial step in developing AI systems for tasks like image recognition, natural language processing, and recommendation algorithms.

More about Ai Tagging jobs
What cities are hiring for Ai Tagging jobs? Cities with the most Ai Tagging job openings:
What are the most commonly searched types of Ai Tagging jobs? The most popular types of Ai Tagging jobs are:
What states have the most Ai Tagging jobs? States with the most job openings for Ai Tagging jobs include:
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Ai Tagging jobs near you
Infographic showing various Ai Tagging job openings in the United States as of June 2026, with employment types broken down into 91% Full Time, and 9% Contract. Highlights an 82% In-person, and 18% Remote job distribution, with an average salary of $114,320 per year, or $55 per hour.

AI Solutions Engineer

Next League, LLC

New York, NY • On-site

$200/hr

Other

Medical, Retirement

Posted 25 days ago

Job description

This role begins as a contract position at an hourly rate of $200 USD per hour, providing a streamlined path toward a permanent, salaried full-time transition. Please note that while the contract phase offers a higher hourly rate in lieu of benefits, the full-time conversion introduces a comprehensive total rewards package, including premium health coverage and retirement programs, alongside a restructured annual salary.

Next League is seeking an AI Solutions Engineer to design, build, and ship production AI systems across our portfolio of sports, entertainment, and league clients. This is the engineering counterpart to our Data Scientist and AI Product Manager roles, and the person who turns prototypes and discovery work into deployed, scaled, and integrated solutions.

The role combines two core capabilities: building AI agents and applied LLM systems, and engineering computer vision pipelines that power fan-facing experiences and content operations. You will work across the full lifecycle, from architecting a solution with a client, to shipping a v1, to integrating it into the client's stack and hardening it for production. You will partner closely with AI Product Managers, data scientists, and client engineering teams. Clients include professional teams, leagues, major sports properties, and national governing bodies.

Projects may span:

  • AI agents, copilots, and assistants for sales, service, content, and internal workflows
  • Retrieval-augmented generation (RAG) and tool-using agent systems
  • Computer vision pipelines for fan-facing experiences (mobile, AR, in-venue activations, second-screen experiences)
  • Computer vision for content operations (highlight detection, automated tagging, content moderation, metadata enrichment)
  • Multi-modal AI systems that combine LLMs and vision models
  • Production deployment, MLOps, and integration into client CRM, ticketing, content, and marketing platforms
  • Evaluation, monitoring, and guardrail systems for agent and CV solutions
Essential Duties and Responsibilities

The following and other duties may be assigned as necessary:

AI Agent & Applied LLM Engineering
  • Build and ship AI agents, copilots, and RAG systems using leading frameworks (LangChain, LangGraph, OpenAI Agents SDK, Claude, and similar)
  • Design tool-use patterns, function calling, and orchestration logic for multi-step agent workflows
  • Develop evaluation rubrics and harnesses for agent quality, covering accuracy, latency, cost, and safety
  • Optimize prompts, retrieval pipelines, and model selection for production performance
Computer Vision Engineering
  • Engineer CV pipelines that power fan-facing experiences across mobile, AR, in-venue, and digital channels
  • Build content operations tooling including automated highlight detection, tagging, moderation, and metadata enrichment for video and image assets
  • Train, fine-tune, and evaluate vision models (object detection, segmentation, classification, embedding, multi-modal)
  • Integrate vision systems with downstream content management, marketing, and fan engagement platforms
Production Systems, Deployment & Integration
  • Deploy AI and CV systems into production environments with appropriate monitoring, logging, and observability
  • Integrate solutions into client systems including CRM (Salesforce, HubSpot), ticketing, content management, and marketing automation platforms
  • Optimize systems for cost, latency, throughput, and reliability
  • Contribute to responsible AI, governance, privacy, and enterprise data handling practices
Client Engagement & Delivery
  • Partner with AI Product Managers and consultants to translate discovery into shippable technical solutions
  • Operate as a hands-on technical lead on client engagements, with occasional embedding for solution design, integration, and hardening
  • Communicate technical tradeoffs to non-technical stakeholders with clarity and credibility
  • Contribute reusable patterns, templates, and reference implementations that scale Next League's delivery across accounts
Qualification Requirements

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

  • 3 to 5 years of experience as an ML engineer, AI engineer, or software engineer shipping production AI/ML systems
  • Strong Python proficiency, with comfort in at least one of TypeScript, Node.js, or Go for production services
  • Hands-on experience building and deploying AI agents, copilots, or RAG systems using modern agentic frameworks
  • Hands-on experience building production computer vision systems, including model training, fine-tuning, or evaluation
  • Demonstrated ability to take AI/ML work from prototype to production with appropriate monitoring and reliability practices
  • Experience working with large language model APIs and at least one major model provider (Claude, OpenAI, Gemini, or similar)
  • Strong communication skills and the ability to operate in client-facing technical conversations
  • Comfort with system design fundamentals: APIs, authentication, retrieval, event tracking, queues, and storage
Preferred Experience
  • Sports, entertainment, league, media, or content industry experience strongly preferred
  • Hands-on experience with leading AI platforms such as:
    • Claude
    • ChatGPT / OpenAI APIs
    • Google Gemini / Vertex AI
    • Microsoft Copilot (M365 / Copilot Studio)
    • AWS Bedrock
    • Azure AI Foundry
  • Experience with agentic frameworks (LangChain, LangGraph, OpenAI Agents SDK, CrewAI)
  • Experience with video processing pipelines, automated highlight generation, or content moderation systems
  • Experience with AR or mobile computer vision (ARKit, ARCore, WebAR, on-device inference)
  • Familiarity with vector databases (Pinecone, Weaviate, pgvector) and embedding workflows
  • Experience with cloud platforms (AWS, GCP, Azure) and MLOps tooling
  • Familiarity with marketing, CRM, ticketing, content management, or fan engagement platforms
  • Experience with automation tools such as n8n or Zapier
  • Background in responsible AI practices, model governance, and enterprise data handling
Who Will Thrive in This Role
  • Engineers who love shipping, and who measure their work in production systems and business outcomes
  • Builders who can move fluently between agent orchestration and computer vision pipelines
  • Pragmatists who can architect cleanly, ship fast v1s, and harden them into production
  • Practitioners excited to apply AI across fan experiences, content operations, and live sports environments

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