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Data Annotation For Ai Jobs in Nevada (NOW HIRING)

Pwc

AI Engineer

Las Vegas, NV · On-site

$50K - $112K/yr

... views for analysis - Building and maintaining data pipelines to support AI model deployment - Applying complex data analysis techniques to discern patterns and trends - Collaborating with team ...

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Data Annotation For Ai information

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.

What are the key skills and qualifications needed to thrive as a data annotation specialist for AI, and why are they important?

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

What is the difference between Data Annotation For Ai vs Data Labeler?

AspectData Annotation For AiData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

What are popular job titles related to Data Annotation For Ai jobs in Nevada?

For Data Annotation For Ai jobs in Nevada, the most frequently searched job titles are:

What job categories do people searching Data Annotation For Ai jobs in Nevada look for?

The top searched job categories for Data Annotation For Ai jobs in Nevada are:

What cities in Nevada are hiring for Data Annotation For Ai jobs?

Cities in Nevada with the most Data Annotation For Ai job openings:

Data Annotation For Ai jobs near you
Infographic showing various Data Annotation For Ai job openings in Nevada as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Lead AI Security Engineer

Credit One Bank

Las Vegas, NV

Full-time

Re-posted 5 days ago

Job description

Position Summary 

We are seeking an experienced AI Security Engineer to lead the design, implementation, and operationalization of security controls for our LLM-powered applications, AI platforms, and model-hosting infrastructure. This role will focus on protecting AI systems from prompt injection, sensitive data leakage, insecure tool use, model abuse, and cloud/infrastructure threats, while helping establish secure engineering patterns for the next generation of AI-enabled products. The role sits at the intersection of Application Security, Cloud Security, and AI Platform Engineering.

The ideal candidate combines offensive and defensive security expertise with strong experience in secure system design, cloud infrastructure, CI/CD, containers, and modern software delivery pipelines. You will help define and execute new AI security initiatives across the enterprise, especially in a regulated financial services environment where confidentiality, resilience, governance, and auditability are critical. Sector-specific AI governance and risk management expectations are increasingly being formalized through frameworks such as the NIST AI RMF.

Essential Job Functions
  • Lead security initiatives for LLM-powered applications, copilots, agentic systems, and AI-assisted workflows.
  • Design and implement controls to reduce the risks of prompt injection, sensitive information disclosure / data leakage, improper output handling, excessive agent autonomy, model and AI supply chain risk, vector / embedding and retrieval-related weaknesses.
  • Partner with engineering teams to embed secure-by-design patterns into AI application development, deployment, and operations.
  • Create guardrails for AI systems that process customers, employees, and regulate financial data.
  • Architect and implement AI gateway controls that centralize security policy enforcement for model traffic, including prompt inspection, response filtering, PII/secret redaction, model access control, rate limiting / abuse prevention, audit logging and evidence generation.
  • Define runtime security policies for AI interactions across internal applications, APIs, tools, and model providers.
  • Build detection and response capabilities for malicious prompts, unsafe model behavior, and data exfiltration attempts.
  • Secure systems built on the Model Context Protocol (MCP) and related AI tool-integration patterns.
  • Define security requirements for MCP servers, proxy servers, and tool connectors, including authentication and authorization, least privilege, schema/input validation, secrets management, network isolation, sandboxing, logging and auditability, third-party server risk review.
  • Assess and mitigate risks associated with MCP architecture, including authorization flaws, confused-deputy scenarios, and unsafe execution paths.
  • Partner with Cloud Security, Platform Engineering, and DevOps to implement hardening, segmentation, identity controls, observability, and incident response readiness.
  • Work closely with Security, Engineering, Legal, Compliance, Risk, and Product teams to align AI security controls with regulatory and internal risk expectations.
  • Help define AI security standards, reference architectures, guardrails, and review processes appropriate for a financial industry environment.
Position Requirements
  • 5+ years of experience in cybersecurity with a strong mix of offensive and defensive security tactics.
  • Deep familiarity with the OWASP Top 10 for LLM Applications
  • Hands-on experience with Docker, Kubernetes, and Cloud Security (AWS/Azure/GCP).
  • Experience designing and securing AI gateways, API proxies, or centralized policy enforcement layers for AI workloads.
  • Working knowledge of MCP server security and secure tool-integration patterns, including identity, authorization, validation, proxy risks, and logging.
  • Strong communication skills with the ability to work across Application Security, Cloud Infrastructure, Platform Engineering, and Product Engineering teams.
  • Familiarity with AI red teaming methodologies and adversarial testing for LLM applications.
  • Ability to translate complex AI risks into actionable technical requirements for developers and executive stakeholders.
  • Familiarity with regulatory frameworks (e.g., NIST AI RMF, ISO/IEC 42001)

Credit One Bank, N.A. is a data-driven financial services company based in Las Vegas. Founded in 1984, Credit One Bank offers a spectrum of credit card products for people in all stages of financial life. Credit One Bank is an equal opportunity employer committed to diversity and inclusion and does not discriminate against any employee or applicant for employment because of age, race, religion, color, disability, sex, sexual orientation, or national origin. Reasonable accommodations can be made for those who require them, including access to job applications and workplace accommodations. Employment at Credit One Bank is based on mutual consent (also known as at-will). This means that employees and the Bank may terminate the employment relationship at any time, with or without cause and with or without notice. Please contact the recruiter for this position to learn more. Credit One Bank does not accept unsolicited resumes from agencies and is not responsible for related fees.

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