• Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. • Labeling elements of a piece of content rather than the content as a whole. • Assigning predefined categories or labels to items. • Evaluating the perceived quality and/or appropriateness of content • Generating labels to advance understanding of a concept, trend etc. • Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity. • Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. • Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. • Ordering or ranking items based on a set of preferences or criteria. • Creating prompts or questions that will be used to generate responses from a language model or other AI system. • Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). • Generating responses to prompts or questions using a language model or other AI system. • Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. • Producing concise summaries of longer pieces of text or data. • Converting spoken language or audio content into written text. • Converting text or spoken language from one language to another. • Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content.

• Own the evolution and growth of Livefront’s AI & Automation service line, including: AI Strategy & Readiness, Agentic Software Engineering, Enterprise Agents & Workflow Automation, AI Platforms & Context Services Engineering, AI Product Development, and AI Training & Enablement. • Own the full portfolio of AI workstreams across the company, ensuring alignment, prioritization, and execution across internal enablement, platform/IP development, client delivery, and market positioning. • Define what “excellent” looks like for AI delivery at Livefront: playbooks, reference architectures, evaluation approaches, governance, security posture, and operational standards. • Recruit, mentor, and develop a team of AI leaders and practitioners; establish career pathways and raise the technical bar across the organization. • Help Livefront evolve all service lines (Digital Strategy & Product, Data & Analytics, Experience Design, Software Engineering, Data & Analytics) to better leverage AI. • Track the market direction of AI—platforms, models, tooling, regulations, and emerging patterns—and translate that into clear, differentiated offerings clients can buy. • Partner with Livefront leadership to mature our go-to-market approach for AI services: positioning, packaging, sales enablement, pursuit strategy, and thought leadership. • Build and manage strategic partnerships across AI frontier labs (e.g., OpenAI, Anthropic), data platforms (e.g., Databricks, Snowflake), and cloud providers (e.g., AWS, Microsoft).

• Independently design and develop AI architectures tailored to individual client requirements • Lead and execute AI projects: from initial requirements analysis to production rollout; depending on project scope, this may include close collaboration with our offshore development team • Prepare and conduct technical workshops, e.g., to identify AI use cases with clients and derive technical roadmaps • Evaluate and select suitable AI technologies, models, and tools for concrete business requirements • Advise clients on technical feasibility, scalability, and sustainable integration of AI solutions into existing IT landscapes • Translate complex technical topics into functioning, secure, and sustainable AI products • Continuously learn about current developments in the AI field as well as new technologies, frameworks, and approaches

• Evaluate AI-generated responses using professional email history and account activity. • Assess outputs for **accuracy, relevance, helpfulness, and personalization quality**. • Identify unsupported assumptions, incorrect personalization, and irrelevant recommendations. • Compare multiple responses and select the stronger overall result. • Provide clear and structured evaluation feedback. • Follow detailed project guidelines and quality standards. • Maintain confidentiality and follow all data privacy requirements. • Complete assigned evaluations accurately and within project timelines.