• Solve high-impact problems across product and internal systems, identifying where AI or automation can create real value. • Work closely with engineering, product, and business stakeholders to understand problems, clarify goals, and define the right approach. • Design pragmatic, end-to-end solutions from architecture and technical plans to production-ready systems. • Build systems involving LLMs, integrations, and multi-step or agentic workflows. • Make sound engineering decisions across model choice, orchestration, evaluation, latency, cost, reliability, security, and maintainability. • Write production-quality code, build prototypes, and evolve them into robust, scalable solutions. • Influence technical direction across teams, define best practices, and help others adopt AI effectively.

Job Title: Generative AI Associate (English) Location: Fully Remote within the U.S. (excluding Alaska, California, Colorado, Nevada, Puerto Rico) Employment Type: Flexible Part-Time Role (part-time, up to 28 hours weekly) Please note, this position is being advertised for future opportunities; while there is no immediate opening, we are proactively building a pipeline of qualified candidates for upcoming needs. Who we are: Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are an AI technology solutions provider-of-choice for 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine. By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of AI. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms. Our global workforce includes over 7,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We’re poised for a period of explosive growth over the next few years. About the Role: At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs). We’re on the lookout for smart, savvy, and curious Generative AI Specialist to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program. This is not a traditional full-time role. It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule. Whether you're a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development while maintaining control over your time. You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance. This is more than just a gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms. What You’ll Be Doing: Core tasks would include (any/multiple of) but not limited to the following: - Evaluation: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions. - Annotation Labeling: Labeling elements of a piece of content rather than the content as a whole. - Classification: Assigning predefined categories or labels to items. - Content Quality: Evaluating the perceived quality and/or appropriateness of content - Content Understanding: Generating labels to advance understanding of a concept, trend etc. - Data Augmentation: 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. - Grading: Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines. - Identification Labeling: Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content. - Preference Ranking: Ordering or ranking items based on a set of preferences or criteria. - Prompt Generation: Creating prompts or questions that will be used to generate responses from a language model or other AI system. - Relevance Evaluation: Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.). - Response Generation: Generating responses to prompts or questions using a language model or other AI system. - Response Rewrite: Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. - Response Summarization: Producing concise summaries of longer pieces of text or data. - Similarity Evaluation: Projects where content is compared in order to drive a determination. - Transcription: Converting spoken language or audio content into written text. - Translation: Converting text or spoken language from one language to another. - Data Collection: 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. Minimum Qualifications: - A Bachelor’s degree or higher is required. Advanced degrees are strongly preferred (Master’s or PhD) - Professional or Expert level proficiency (C1/C2) in English Hourly Range: $15.00 USD Hourly rates at Innodata vary depending on a wide array of factors, which may include but are not limited to the role, skill set, educational background and geographic location. Innodata is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity or expression, sexual orientation, age, marital status, veteran status, disability status, or any other legally protected status. Innodata is committed to creating an inclusive environment for all employees and applicants. If you need assistance or accommodation during the application or recruitment process due to a disability, please contact us and we will be happy to assist. Applicants must be legally authorized to work in the United States at the time of hire. Innodata is unable to provide visa sponsorship now or in the future for this position.

• Build AI-assisted tooling for engineering workflows • Improve developer productivity and operational efficiency • Design systems that reduce repetitive manual engineering work • Develop AI-driven solutions for pipeline analysis, failure classification, flaky test detection, release validation, and more • Improve reliability and visibility of engineering pipelines • Build AI systems for troubleshooting assistance and operational workflows • Design and implement engineering copilots and internal engineering assistants • Deploy and maintain production-grade AI services

Pierce Technology Corp is seeking an AI Engineer - SDLC Process Improvement to enhance our software development lifecycle (SDLC) by integrating cutting-edge AI technologies and automating key processes. This role focuses on leveraging AI to streamline development workflows, improve code quality, and accelerate delivery timelines across engineering teams. Key Responsibilities: - Analyze current SDLC processes to identify opportunities for AI-driven improvements and automation. - Design and implement AI-powered tools to support code review, testing, deployment, and monitoring. - Collaborate with software engineering, QA, and DevOps teams to integrate AI solutions seamlessly into existing workflows. - Develop metrics and evaluation frameworks to measure the effectiveness and impact of AI enhancements on SDLC efficiency. - Drive continuous improvement initiatives by leveraging data-driven insights and A/B testing of AI tools within the development lifecycle. This role requires a blend of AI engineering expertise, process analysis skills, and cross-functional collaboration to transform the way our engineering teams build and deliver software.