InnoData

Solutions Engineer

• Configure and validate native AI-assistive features across bundled platform components (Dataset Explorer, DataCard Service, Annotation Platform)
• Integrate and tune SAM 2 for full-motion video annotation: object tracking, segmentation calibration, confidence threshold configuration
• Implement Frontier model API integration for synthetic data fidelity validation: prompt engineering, response validation, quality scoring
• Configure AI-assisted annotation features: confidence scoring, auto-escalation triggers, model-assisted label suggestion
• Implement ICAM / OIDC authentication integration with AFS identity framework
• Configure data-layer DLP policies above the AFS-managed DLP infrastructure substrate
• Configure NiFi FMV codec validation layer (H.264, H.265, MPEG-4) above AFS-managed substrate
• Validate AI feature integration end-to-end across storefront, annotation platform, and DataCard write-back during Phase C.

AI Solutions Engineer

Artificial Intelligence

• 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.

AI Quality Analyst

LLM Engineer

• design and implement the pipelines and tooling that connect data, evaluation, and post-training
• help customers and internal teams move from evaluation findings to measurable model improvements
• build fine-tuning workflows (e.g., supervised fine-tuning and preference-based optimization)
• integrate evaluation harnesses into model development loops
• improve experiment reliability and throughput
• support advanced evaluation scenarios such as long-context, cross-modal, and dynamic multi-turn interactions
• contribute to Innodata’s internal R&D efforts, including benchmark datasets, evaluation frameworks, and reusable infrastructure for model assessment and post-training experimentation.
• Lead or co-lead technically complex ML engineering projects from initial customer discussions through implementation and delivery
• Design, build, and improve LLM training and post-training pipelines, including data ingestion, preprocessing, fine-tuning, evaluation, and experiment tracking
• Implement and optimize evaluation systems for LLMs and multimodal models, including offline benchmarks and task-specific test harnesses
• Integrate human-in-the-loop and AI-augmented evaluation signals into model development workflows
• Build robust infrastructure and tooling for reproducible experimentation, metrics logging, and regression monitoring
• Diagnose model behavior and pipeline failures, including data issues, training instability, metric inconsistencies, and evaluation drift
• Collaborate with Language Data Scientists and Applied Research Scientists to translate evaluation frameworks into executable systems
• Work closely with customer technical stakeholders to understand goals, constraints, and success criteria; propose and implement technically sound solutions
• Contribute to internal research and platform development, including benchmark frameworks, evaluation tooling, and post-training workflow improvements
• Contribute to best practices and standards for LLM training, evaluation, and quality assurance across projects
• Mentor junior engineers and contribute to technical design reviews, documentation, and engineering rigor across the team.

AI/ML Research Engineer, LLM Post-Training – Evaluation

AI Evaluator

• 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.

AI Data Specialist

Content Marketing Manager

AI Content Specialist

AI Content

Account Executive

• New Logo Acquisition: Drive pipeline creation and close $10M+ in net-new enterprise deals annually.
• Solution Selling: Position Innodata’s offerings:
• AI Implementation: Smart search, knowledge assist, copilots, and agentic AI.
• GenAI QA: Red-teaming, model evaluation, safety, and reliability testing.
• GenAI Context Engineering: Structuring enterprise context into AI workflows.
• GenAI Product Operations Support: Scaling model operations, triage, labeling, and developer platform support.
• CxO Engagement: Build trusted relationships with CIOs, CTOs, CDOs, and Innovation leaders to shape AI transformation roadmaps.
• Account Strategy: Map target accounts, orchestrate tailored outreach, and land pilots that expand into enterprise programs.
• Collaboration: Partner with Pre-sales, practice, delivery and solution teams to co-create compelling proposals, POVs, and demos.
• Thought Leadership: Represent Innodata at industry events, AI forums, and analyst discussions to raise market visibility.