Top AI Specialist Jobs in Los Angeles, CA

Label video, image, and metadata content across major social platforms using contextual guidelines. Apply cultural and linguistic judgment, identify edge cases, perform multilingual quality assurance, analyze quality misses, and improve labeling processes. The role requires Hindi and English fluency, careful handling of sensitive content, strong attention to detail, and comfort using AI tools and Google Workspace in a hybrid office environment.

Labels video, image, and metadata content across social media platforms to create ground-truth data for machine learning systems. Applies contextual and cultural guidelines, handles ambiguous or sensitive content, identifies trends and edge cases, performs linguistic quality assurance, analyzes quality misses, and recommends process improvements. Requires Spanish and English fluency, strong attention to detail, familiarity with major social platforms, clear communication, and comfort using AI tools and Google Workspace.

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.

Shape conversational AI behavior by writing prompts, creating and annotating dialogue datasets, automating content workflows, testing model versions, evaluating responses, and identifying whether issues stem from prompts, datasets, or models. Collaborate with ML engineers to improve output quality, analyze user conversations, and maintain standards for tone, style, explicitness, and quality. The role requires strong English writing, hands-on LLM experience, analytical ability, and comfort with adult content.

Develop and deploy Microsoft Cloud solutions using Cloud Foundry, AI, Agentic AI, and Copilot technologies. Collaborate with cross-functional teams, optimize automation and integrations, lead technical design discussions, resolve deployment challenges, monitor industry trends, and provide technical training and mentorship.

Freelance AI Specialist responsible for completing and maintaining a shared business knowledge base, improving AI workflows with Claude and Gemini, creating reusable prompts, connecting AI to business tools, documenting processes, training teams, and reporting progress. The role requires careful fact-checking, strong written communication, confidentiality judgment, basic scripting or automation skills, and self-directed project execution.

Designs, builds, and evaluates complete AI systems for Pearson’s K-12 assessment platform. Responsibilities include predictive machine learning, statistical methods, retrieval, knowledge graphs, generative and agentic AI, deterministic services, human review, and system integration. The role develops evaluation datasets and regression suites, analyzes failure modes, supports model adaptation, monitors deployed systems, and collaborates with engineers, psychometricians, learning scientists, and content experts to create transparent, reliable, and auditable AI capabilities.

Design and build Pearson’s knowledge architecture for AI-powered K-12 assessment and learning systems. Responsibilities include developing knowledge graphs, ontologies, schemas, data pipelines, APIs, retrieval and context architectures, and hybrid AI integrations. The role establishes provenance, versioning, traceability, quality measures, and responsible AI practices while partnering with engineers, AI scientists, psychometricians, learning scientists, and content experts. It requires autonomous innovation, rapid prototyping, rigorous evaluation, and translating educational and assessment expertise into computational representations.

Develop and scale AI, Microsoft Copilot, Power Platform, and intelligent automation solutions across Buildings. Responsibilities include designing AI agents and workflows, identifying automation opportunities, delivering training and adoption programs, supporting governance, integrating Microsoft platforms, and measuring business value through analytics and reporting.

Develop and validate machine learning models, training pipelines, inference systems, and numerical methods using Python. Debug algorithmic and system-level issues, optimize memory usage and performance, design systematic tests, and build reproducible workflows. Review challenging ML engineering tasks, use coding agents in development, document technical findings, and deliver high-quality work independently within deadlines.

Reconstructs and redesigns data-driven presentations in Microsoft PowerPoint, creating visualizations and analytics reports that communicate complex insights clearly. Reviews and edits content for accuracy, flow, and narrative impact; applies data analysis and statistical reasoning; and refines slides based on feedback. The role supports AI training projects through high-quality presentation and analytical deliverables.

Review and annotate robotic-system video footage by identifying objects, movements, events, visual features, and anomalies. Provide feedback to improve video datasets and annotation consistency, collaborate with AI teams on annotation protocols, document observations, participate in quality reviews, and recommend improvements to video capture and labeling strategies.

Evaluate and improve the safety, security, and robustness of large language models. Responsibilities include designing jailbreak, prompt injection, adversarial, and tool-use abuse tests; developing regression suites and evaluation frameworks; identifying vulnerabilities; translating findings into safety recommendations; and documenting methodologies and results for technical and non-technical stakeholders.

Designs, builds, and optimizes AI-driven automation workflows that reduce manual work and improve business efficiency. Responsibilities include integrating CRM systems, APIs, and automation platforms; developing chatbots, AI assistants, and prompt systems; monitoring performance; troubleshooting errors; and collaborating with marketing, operations, and technology teams. The role also requires staying current with AI tools and automation trends.

Perform object detection annotation on visual datasets using digital annotation tools. Label and review objects, apply project guidelines, correct inconsistencies, maintain data quality and confidentiality, and meet productivity standards. The role requires accuracy during repetitive tasks, independent remote work, collaboration with project teams, and a consistent 40-hour weekly schedule.

Develop generative AI solutions using Python, AWS, LLMs, chatbots, prompt and context engineering, RAG, vector databases, and agentic AI frameworks. Build coding prompts and contexts with GitHub Copilot, Claude Code, and Amazon Q Developer to convert business stories into production code and pull requests. Design autonomous and multi-agent workflows, memory and token-management systems, caching strategies, MCPs, and real-time AI decisioning features. Auto-lending experience and A/B testing expertise are advantageous.

Lead design and implementation of AI and data governance frameworks, privacy guardrails, metadata standards, and compliance with GDPR/HIPAA/CCPA/NIST AI RMF. Monitor and audit models for bias, explainability, and transparency; partner with engineers, stewards, and leaders to embed ethical decision-making, set KPIs, run audits, and produce dashboards and reports to drive governance adoption.

Advises County departments on responsible enterprise AI adoption, including readiness assessments, use-case prioritization, governance aligned with the NIST AI Risk Management Framework, proof-of-concept oversight, output validation, standards development, stakeholder workshops, training, documentation, and measuring adoption, value, and ROI.

Annotate construction drawings using bounding boxes, polygons, and segmentation masks for computer vision models. Maintain consistent, accurate labels, clean datasets, identify ambiguous cases, and help improve annotation guidelines. Meet weekly quality and throughput targets while communicating data issues to ML engineers. The role requires focused repetitive work, precise instruction-following, independent remote work, and clear written English. It is a remote, 3–6 month contract with potential full-time conversion.

Supports responsible and scalable AI adoption by coordinating intake, inventory, governance documentation, risk reviews, approvals, vendor and regulatory records, project milestones, training, monitoring, reporting, dashboards, workflows, and issue escalations. Partners with clinical, operations, compliance, HR, IT, security, EMR, analytics, and finance teams to enable safe, compliant AI deployment across the organization. Travel to satellite and other locations is required.

Leads the firm's AI strategy and implementation initiatives by identifying automation opportunities, building AI-powered workflows and agents, integrating AI tools with existing systems, and optimizing solutions across legal and business functions. The role also trains employees, develops documentation and SOPs, manages projects through implementation, and drives organization-wide adoption of AI technologies.

Coordinates data sourcing, vendor and partner relationships, dataset evaluation, onboarding into AI pre-training pipelines, and data governance tracking. Maintains documentation, dashboards, delivery timelines, quality metrics, licensing records, and compliance logs while facilitating communication among AI research, legal, privacy, security, and external data-provider teams.

Designs and demonstrates computer vision solutions for customer opportunities, including model selection, data strategy, evaluation, edge deployment, and business-case development. The role leads discovery, high-level architecture, proofs of concept, demos, partner engagement, and technical enablement. It requires hands-on expertise across classical computer vision, deep learning, video intelligence, vision language models, model lifecycle management, and edge optimization, plus strong customer-facing and commercial communication skills.

Build functional AI prototypes and MVPs using low-code platforms such as Copilot Studio and Power Platform. Translate business needs into technical specifications, user stories, and workflows; partner with engineering to scale validated concepts into production. Roll out AI productivity tools, support go-to-market and customer success workflows, measure adoption and impact, and apply AI security, privacy, governance, and compliance principles while coordinating with product, architecture, legal, and security stakeholders.

Annotates data accurately according to project guidelines, performs quality checks, provides feedback on annotation tools and processes, collaborates with project teams, maintains efficiency, and reports issues for resolution.