InnoData

• Lead technical discovery with prospective and existing customers — foundation model labs, frontier AI teams, and large enterprises — to understand model objectives, gaps, and constraints. • Design end-to-end solutions across the post-training stack: SFT data curation, preference data collection for RLHF/DPO, golden datasets, custom benchmarks, LLM-as-judge pipelines, human-in-the-loop evaluation, red teaming, and multimodal eval (text, image, audio, video, long-context). • Architect engagements that combine Innodata’s platforms (GenAI Test & Evaluation Platform, Annotation Platform, GenAI Workbench) with our global SME workforce across 85+ languages and domains. • Author technical proposals, SOWs, solution diagrams, and pricing models in partnership with sales, delivery, and finance. • Run technical workshops, POCs, and pilot designs that de-risk larger programs and prove value quickly. • Serve as the ongoing technical advisor during delivery, partnering with applied research scientists, AI/ML research engineers, language data scientists, and program managers to keep solutions aligned with the original intent. • Feed customer signal back into Innodata’s R&D and product roadmap — what benchmarks customers actually want, where eval methodology is breaking, what new fine-tuning paradigms are gaining traction. • Stay current on the state of the art in evals (e.g., dynamic and agentic benchmarks, capability vs. safety evals, long-context and tool-use evaluation) and post-training (SFT, RLHF, DPO, RLAIF, rejection sampling, distillation). • Represent Innodata externally — at customer reviews, conferences, and in technical content.

• Own, build, and continuously improve sophisticated driver-based financial models spanning the consolidated forecast, annual operating plan, and long-range plan, including scenario, sensitivity, and unit-economics analysis to support executive and Board decisions. • Apply deep familiarity with services / delivery-based business economics — utilization, billable capacity, project margins, revenue recognition timing, and headcount-to-revenue linkage. • Own defined FP&A processes end to end — from data through analysis to reporting — and drive standardization, automation, and repeatable close and forecast cycles rather than relying on ad hoc effort. • Communicate clearly and credibly with senior leadership and executives, translating complex financial analysis into concise, decision-ready narratives and materials. • Lead the monthly close partnership with Accounting, including variance analysis, revenue reconciliation, and management reporting. • Prepare Board, investor, and executive materials with strict adherence to public-company disclosure discipline. • Partner with operations and delivery leaders to translate operational metrics into financial outcomes. • Support corporate development, including valuation frameworks, diligence support, and deal modeling. • Mentor analysts and contribute to the maturation of the FP&A function.

• 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.

• 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.

• 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. • 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. • 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. • Generating responses to prompts or questions using a language model or other AI system. • Rewriting existing text while preserving the original meaning. • 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.

• 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.

• 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.

• 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.

• 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.

• 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.