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Ai Rater Jobs in Georgetown, TX (NOW HIRING)

Stratedge IT Consulting INC

AI Solutions Architect

Austin, TX · On-site

$62.50 - $82.25/hr

State of TX billing rate 65/hr on c2c 60 on w2 TxDOT has issued a request for an AI Solutions Architect to support Data Governance in building an enterprise agentic AI platform for governed data and ...

Apex Systems

AI Business Analyst

Austin, TX · On-site

Job#: 3040922 AI Business Analyst Location: Austin, TX Schedule: Hybrid. 3 days in office, 2 days ... Message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You ...

Realtor.com Careers

Sr. AI Platform Engineer

Austin, TX · On-site

$103K - $142K/yr

... rate limiting, and PII controls. * Enforce Platform Governance via Code: Implement identity ... Build the AI platform. Help create a greenfield AI platform and the lifecycle around it so teams ...

Judge Group, Inc.

Senior Java AI Developer

Austin, TX · On-site

$70 - $75/hr

Our client is currently seeking a Senior Java AI Developer Duration : 6 months with possibility of ... Message & data rates apply and message frequency may vary. Consistent with Judge's Privacy Policy ...

Judge Group, Inc.

Senior .NET AI Developer

Austin, TX · On-site

$63 - $68/hr

NET AI Developer Location : On-site in Southlake, TX or Austin, TX Duration : 6 months with ... Message & data rates apply and message frequency may vary. Consistent with Judge's Privacy Policy ...

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Ai Rater information

What is an AI Rater?

An AI Rater evaluates and provides feedback on artificial intelligence models, typically improving search engines, chatbots, or recommendation systems. They assess the relevance, accuracy, and quality of AI-generated content based on specific guidelines. This role requires strong analytical skills, attention to detail, and familiarity with the subject matter being reviewed. AI Raters often work remotely and on a flexible schedule.

What skills and qualifications are needed to thrive as an AI Rater?

To thrive as an AI Rater, you generally need strong attention to detail, analytical thinking, and proficiency in English, often supported by formal education such as a high school diploma or higher. Familiarity with web browsers, online research, and company-specific rating platforms or guidelines is essential. Excellent time management, adaptability, and effective written communication help individuals excel in this position. These skills and qualities ensure accurate and consistent evaluations of AI-generated content, directly impacting the improvement of artificial intelligence systems.

What does an AI Rater do?

A typical day for an AI Rater involves reviewing and evaluating various types of content, such as search engine results, social media posts, advertisements, or chatbot responses, to ensure they meet quality and relevancy standards. You may follow detailed guidelines to rate or annotate content, complete assigned tasks in a web-based platform, and provide feedback to help improve AI performance. Most positions are remote and offer flexible schedules, allowing you to plan your workload around personal commitments. Collaboration is generally limited, as most work is performed independently, but periodic communication with team leads for training or updates is common.

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Cities near Georgetown, TX with the most Ai Rater job openings:

Ai Rater jobs near you

AI Solutions Architect

Stratedge IT Consulting INC

Austin, TX • On-site

$62.50 - $82.25/hr

Contractor

Posted 5 days ago

Job description

Notes:

Only locals to Texas with a Texas ID will be accepted.

No relocation candidates will be considered.

Finalized candidates must show a Texas DL in a video meeting for screenshot capture as part of the compliance process.

 

State of TX

billing rate 65/hr on c2c 60 on w2

TxDOT has issued a request for an AI Solutions Architect to support Data Governance in building an enterprise agentic AI platform for governed data and AI workflows. The work is centered on architecting production multi-agent systems, reusable AI agents, Snowflake-based data solutions, agent registries, MCP integrations, observability, and human-in-the-loop controls. The ideal candidate has 10+ years in software or data engineering, has led a multi-agent platform running in production for at least 12 months, and brings deep hands-on experience with Snowflake, Python, SQL, agent evaluation, LLM observability, security controls, and production-grade AI orchestration.

Responsibilities include (but are not limited to):

  • Architect the enterprise agentic AI platform: Design reusable task-specific agents and production multi-agent workflows with planner/worker orchestration, tool calling, state and memory management, error recovery, and an enterprise Agent Registry or Catalog.
  • Build governed AI infrastructure: Implement SSO, RBAC, MCP/OpenAI-compatible tool interfaces, sandboxed agent-generated code execution, approval workflows, audit logging, guardrails, rollback procedures, and human-in-the-loop controls.
  • Establish production operations and observability: Implement agent evaluation frameworks, regression testing, release quality gates, per-run tracing, token and cost monitoring, failure analysis, and integrations across Snowflake, data engineering, CI/CD, and enterprise systems.

Minimum Candidate Characteristics:

  • 10+ years in software or data engineering, including at least 3 years building LLM-based systems and 2+ years operating agentic AI systems in production serving live business users or workloads; prototypes, pilots, demos, and basic RAG chatbots do not qualify.
  • Must have served as lead architect for at least one multi-agent system operating in production for 12+ months, with direct ownership of orchestration, tool calling, state/memory management, error recovery, and an Agent Registry or Catalog.
  • Strong production experience with Python, SQL, Snowflake/data platforms, CI/CD, LLM evaluation and observability, MCP or OpenAI-compatible interfaces, SSO/SAML/OIDC, RBAC, guardrails, sandboxed code execution, and human approval workflows. 

 

Exceptional Candidate Characteristics:

  • Experience with one or more Texas State Agencies.

 

Responsibilities:

Architect and establish the platform's four foundational pillars:

  • Reusable Foundational Agents: Design modular, task-specific AI agents that can be chained together to handle complex data lifecycle tasks.
  • Enterprise Applications: Build and deploy user-facing agentic workflows tailored to enterprise needs.
  • Balanced Agent Governance: Implement enterprise-grade security including Single Sign-On (SSO), Role-Based Access Control (RBAC), Model Context Protocol (MCP) or OpenAI-compatible standards, and a centralized Agent Catalog.
  • Observability & Human-in-the-Loop Controls: Integrate comprehensive monitoring, logging, and guardrails to ensure reliability, transparency, and essential human oversight.

Qualifications & Skills

  • Snowflake Mastery: Deep, hands-on experience architecting complex data solutions within the Snowflake ecosystem (including Snowpark, Streamlit, and Cortex AI).
  • Agentic AI & LLMs: Proven track record of developing agentic frameworks, multi-agent orchestration, and leveraging open standards (MCP, OpenAI-compatible APIs).
  • Data Engineering Infrastructure: Expertise in DBT, SQL, Python, and orchestrating modern ETL/ELT pipelines.
  • Enterprise Security: Strong understanding of IAM, SSO, RBAC, and governance frameworks in public sector or highly regulated environments.
  • Collaboration: Excellent communication skills to work closely with data engineers, architects, and business stakeholders.

 

Minimum (Required):

YearsSkills/Experience10Experience in software or data engineering3Experience building LLM-based systems2Experience designing and operating agentic AI systems in production — systems serving live business users or workloads. Prototypes, pilots, internal demos, and RAG chatbots do not meet this bar. Served as the lead architect of at least one multi-agent system that has run in production for 12+ months, with direct ownership of supervisory/planner-worker orchestration, tool calling, state and memory management, and error recovery for long-running workflows. Prior experience building Agent Registry or Catalog Production experience with agent-generated code that executes: sandboxed execution, automated validation and testing of generated artifacts, and engineer review-and-approve workflows gating deployment. (Directly relevant — this platform generates executable ingestion code and DBT packages.) Built and operated agent evaluation harnesses in production: offline eval suites, regression testing for prompt and model changes, and measurable quality gates that block release on failure. Operated LLM observability in production: per-run tracing of agent decisions and tool calls, token and cost monitoring, and hands-on triage of agent failures and incidents. Implemented guardrails and human-in-the-loop controls in a governed environment: approval gates, permission-scoped tool access for agents, audit logging, and rollback procedures Has built or deployed MCP servers/clients or OpenAI-compatible tool interfaces in a production system — not just consumed a vendor API. Strong Python and SQL; CI/CD for data platforms; SSO (SAML/OIDC) and RBAC design. Experience with Snowflake (Snowpark, Streamlit, Cortex AI)