Job Title: Generative AI Specialist - Bilingual (French and English) Location: Fully Remote within the Canada (excluding Quebec) Employment Type: Part-Time; up to 28 hours per week Posting Status: This job posting is for future opportunities and does not represent an existing vacancy. Applications will be considered as roles become available. 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 in a humanities specialization is required. Advanced degrees are strongly preferred (Master’s or PhD) - Professional or Expert level proficiency (C1/C2) in English and French Salary 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. Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.

Chief Architect AI Threat Detection & Response Location Remote – US or UK preferred Function Office of the CTO, Threat Detection & Intelligence Reports To CPTO or VP of Threat Detection & Intelligence About the Role The threat landscape is undergoing a fundamental shift. Adversaries are weaponizing AI — using large language models to craft hyper-personalized phishing at scale, injecting malicious instructions into agentic workflows, and deploying deepfake personas to bypass human judgment. Mimecast needs an architect who sees this clearly and knows how to build detection systems that stay ahead of it. The Chief Architect for AI Threat Detection & Response is a senior individual contributor role within the Office of the CTO, with the potential to expand into a managerial role leading a small incubation team of developers as the function matures. You will define the technical blueprint for how Mimecast detects and responds to next-generation threats — combining LLM-based detection, behavioral anomaly models, and AI-specific attack surface coverage — across email, collaboration, and human risk signals at enterprise scale. The Threat Surface You’ll Own This role is explicitly scoped to emerging and AI-driven threats, not just traditional email security. You will architect detection for: - AI-generated phishing and BEC — LLM-crafted lures that defeat signature and heuristic-based detection, including persona impersonation and synthetic voice/video in hybrid attacks. - Prompt injection attacks — adversarial instructions embedded in emails, documents, or web content designed to hijack Mimecast’s own AI pipelines or customer-deployed LLM agents. - Agentic workflow abuse — manipulation of AI agents operating on behalf of users (auto-reply, scheduling, data retrieval) to exfiltrate data or pivot laterally without human interaction. - AI-assisted reconnaissance and evasion — attackers using models to profile targets, time campaigns, and dynamically mutate payloads to avoid detection. - Deepfake and synthetic identity threats — AI-generated audio, video, or identity signals used in spear-phishing, vishing, and wire fraud scenarios. - Model poisoning and adversarial ML — attacks targeting Mimecast’s own detection models through crafted inputs designed to degrade accuracy or induce false negatives. What You’ll Do LLM-Based Detection Architecture - Design and own the architecture for LLM-powered detection pipelines — including prompt design, context assembly, model selection (hosted vs. fine-tuned), and inference cost/latency trade-offs at email scale. - Define where LLMs augment vs. replace classical ML models in the detection stack: semantic intent analysis, writing style anomaly, social engineering classification, and zero-day lure identification. - Build the adversarial robustness framework for Mimecast’s LLM-based detectors — red-teaming pipelines, prompt injection hardening, and evasion-resistance testing. - Establish evaluation methodology for LLM detectors: beyond accuracy metrics to include hallucination rate, decision consistency, and explainability for analyst review. Anomaly Detection & Behavioral Modeling - Own the architecture for behavioral baseline modeling across users, communication graphs, and sending infrastructure — enabling detection of deviations that precede BEC, account takeover, and insider threats. - Design unsupervised and semi-supervised anomaly detection systems that operate on high-cardinality, sparse behavioral signals without requiring labeled attack data. - Architect multi-signal correlation across email, identity, endpoint, and SaaS telemetry to surface low-and-slow attacks invisible to single-channel detectors. - Define feedback mechanisms between analyst verdicts and anomaly model recalibration — ensuring drift is detected and baselines evolve with customer communication patterns. AI-Specific Attack Surface Coverage - Define Mimecast’s technical posture on prompt injection detection — both as a threat to customer AI deployments and as a risk vector within Mimecast’s own agentic features. - Architect detection for agentic workflow abuse scenarios: anomalous agent actions, out-of-policy tool calls, and AI-to-AI communication patterns that indicate compromise. - Build the threat model and detection coverage map for synthetic content (deepfakes, AI-generated documents, cloned sender identities) as these become primary attack delivery mechanisms. - Engage with the security research community and contribute to emerging standards for AI threat taxonomy, attack surface enumeration, and detection benchmarks. Platform, Standards & Leadership - Partner with Platform Services to ensure detection infrastructure — model serving, feature stores, real-time inference — is a first-class component of the Arc platform data fabric. - Define engineering standards for model evaluation, adversarial testing, drift monitoring, and incident response when detection models degrade or are actively attacked. - Mentor senior engineers and ML practitioners; set the technical bar for the detection organization and act as the internal authority on AI-native threat research. - Represent Mimecast externally — at RSAC, Black Hat, and industry forums — as a recognized voice on AI threat detection and the evolving AI attack surface. Threat Evangelization & Intelligence Publishing - Own the ‘anatomy of a threat’ narrative for significant detections — translating raw detection data into structured threat breakdowns that explain attack mechanics, targeting patterns, evasion techniques, and impact across the Mimecast customer base. - Publish threat intelligence in formats designed for multiple audiences: technical deep-dives for the security research community, threat briefings for enterprise customers and prospects, and sales-ready threat narratives that demonstrate Mimecast’s detection advantage in active campaigns. - Work directly with GTM, PMM, and sales engineering to package threat data as evidence of detection efficacy — turning real-world catches into competitive differentiation in RFPs, customer briefings, and analyst interactions. - Build and maintain a cadence of threat reporting — monthly threat digests, campaign-specific advisories, and annual threat landscape reports — that establishes Mimecast as a primary source of AI threat intelligence. Closed-Loop Detection Pipeline - Architect the end-to-end feedback loop from missed detections and customer-reported false negatives back into the detection engines — ensuring every evasion event is a structured input to model improvement, not a one-off incident. - Design the threat intelligence ingestion pipeline: how external threat feeds, analyst verdicts, customer submissions, and Mimecast’s own detection corpus flow into feature engineering, model retraining, and rule updates in a governed, auditable way. - Define the data model and tooling for missed detection triage — classification of why a threat was missed (signature gap, model blind spot, novel evasion), routing to the correct remediation path, and tracking time-to-coverage for each gap type. - Build the operational cadence around the loop: detection gap reviews, retraining triggers, coverage regression testing, and SLA targets for closing gaps on newly identified attack patterns. What You Bring - 15+ years in security architecture or applied ML, with at least 5 years building production AI/ML detection systems — not just models in research, but systems operating at scale under adversarial conditions. - Demonstrated expertise in LLM application design: prompt engineering, RAG architectures, fine-tuning, and the failure modes specific to LLMs deployed in security-critical pipelines. - Deep understanding of the AI threat landscape — prompt injection, adversarial ML, model evasion, synthetic content attacks — and the detection approaches that work against each. - Hands-on experience with anomaly detection at scale: unsupervised methods (autoencoders, isolation forests, graph anomaly detection), behavioral baselining, and multi-variate signal correlation. - Strong command of cloud-native ML infrastructure on AWS — SageMaker, Kinesis, Bedrock, or equivalent — with real architectural opinions on latency, throughput, and cost at email volume. - Proven track record of external influence: CVEs, published research, conference presentations, or recognized contributions to AI security standards and threat intelligence. - Ability to operate as a senior IC in a PE-backed environment — decisive under ambiguity, outcome-focused, and capable of holding an architectural position with rigor and humility. What Success Looks Like (Year 1) - Published a comprehensive AI threat detection architecture — covering LLM-based detection, anomaly models, and AI-specific attack surfaces — adopted as the engineering reference across all detection squads. - Shipped at least one production LLM-based detector with documented precision/recall, adversarial robustness profile, and analyst explainability output. - Defined Mimecast’s detection posture for prompt injection and agentic abuse, with coverage mapped to the ARC (Agentic Risk Center) platform’s agentic feature roadmap. - Established adversarial red-teaming as a standard part of the detection model release process, with a recurring cadence and documented evasion test suite. - Launched a threat intelligence publishing cadence — at least one major ‘anatomy of a threat’ report and a suite of sales-ready threat narratives actively used by GTM in competitive deals. - Delivered a functioning closed-loop pipeline from missed detections and threat intel back into detection engines, with defined triage classification, remediation routing, and time-to-coverage SLAs. - Recognized by at least two enterprise customers and one industry analyst as a credible technical authority on AI-native threat detection. Mimecast is backed by Permira and serves ~42,000 customers globally. The ARC platform combines email security, human risk management, archiving, and insider risk into a unified security fabric. This role is a rare opportunity to define AI detection architecture at scale, with direct exposure to the CPTO and executive team. The base salary range for this position is $228,000−$342,000 plus benefits. This range represents the minimum and maximum new hire compensation for this role. The position may also be eligible for incentive plans and additional benefits, in accordance with company policy and local regulations. Our salary ranges are determined by role, level, and location with individual compensation also dependent on factors such as qualifications, experience, and skills. Final offers will reflect these considerations and may vary accordingly. Belonging at Mimecast Cybersecurity is a community effort. That’s why we’re committed to building an inclusive, diverse community that celebrates and welcomes everyone – unless they’re a cybercriminal, of course. We’re proud to be an Equal Opportunity and Affirmative Action Employer, and we’d encourage you to join us whatever your background. We particularly welcome applicants from traditionally underrepresented groups. We consider everyone equally: your race, age, religion, sexual orientation, gender identity, ability, marital status, nationality, or any other protected characteristic won’t affect your application. If you require any adjustments or accommodations due to a disability, or any other reason that may help you in your interview process, please let us know by emailing careers@mimecast.com. Due to certain obligations to our customers, an offer of employment will be subject to your successful completion of applicable background checks, conducted in accordance with local law. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment.

• Lead a team of Grading Effectiveness Specialists to ensure consistent, high-quality grading practices. • Own audit strategy, execution oversight, and performance outcomes related to grading quality. • Drive continuous improvement by leveraging data insights and leading design, implementation, optimization of an AI-first grading audit infrastructure. • Define and own the grading audit framework and performance thresholds. • Oversee audit execution for consistency and measurable quality improvement. • Architect and scale AI-powered audit workflows using generative AI platforms. • Manage and develop a team, setting clear expectations and coaching for effectiveness. • Translate audit data into actionable insights and collaborate cross-functionally for improvement initiatives.

About AutoFi AutoFi is the leading provider of digital commerce technology that powers the sales and finance experiences for the most innovative brands and dealers in automotive. The AutoFi platform enables a more transactional buying experience with $4B in funded loans processed through AutoFi annually. AutoFi’s dynamic selling platform empowers dealers to sell vehicles more efficiently and profitably, both online and in the showroom. We are funded for years of future growth and backed by investors including Crosslink Capital, Santander Holdings USA, SVB Financial Group, Ford, BMW iVentures and JP Morgan Chase. Our team is diverse - spread out across the U.S. and Canada, we have backgrounds from finance and technology as well as deep experience in all areas of the auto space. We’re empathetic, gritty, curious, and humble owners of this business and are supported by some of the biggest names in the auto and financial industries as commercial partners. We’ve never been more excited about the opportunity in front of us to help transition the auto industry from offline to online. If changing a trillion-dollar industry sounds exciting, we’d love to hear from you. For more information, visit www.autofi.com. About the Role The Operational Enablement Specialist is expected to play a pivotal role in designing, provisioning and maintaining both foundational and advanced learning experiences that enable both human teams and AI-driven systems. This role will report into the Head of Operations, and partner closely with personnel across the business to own the full lifecycle of enablement content including new hire onboarding, revenue generating activities, Product/Service offerings, operational and technical support activities. You will be responsible for information gathering and analysis through design, development, delivery and continuous improvement across a wide range of topics. This will include employee engagement in growth strategies and behaviors, internal systems and processes, as well as successful Product configuration to meet Dealership needs across varying business models and their requirements. You will create high-quality enablement materials optimized for both human learning and AI comprehension, ensuring consistent, accurate and scalable knowledge, inclusive of training an AI Agent to accurately interpret, reason through, and answer complex operational and industry specific questions. Candidates must be self-driven, have the ability to lead, motivate and drive results within cross departmental teams that they do not directly manage. They must have strong communication and presentation skills, be well organized, attentive to fine details, able to work under pressure and have comfort persevering under ambiguity. Each member of our team is challenged to contribute in a variety of capacities across the entire organization and show their individual strengths, from product to customer experience. Responsibilities - Participate in building a transparent, cohesive and collaborative team environment with a culture of open communication and feedback. - Locate sources, gather & analyze relevant information, inclusive of deep information gathering with subject matter experts, applications/tools, processes, procedures/SOPs and work instructions etc, for the purpose of uncovering implicit knowledge, assumptions, edge cases in order to develop relevant, robust internal training content and AI Agent enablement materials. - Utilize a hands-on approach in the development of training content related to systems, tools and processes to obtain current state, real-world information and examples, asking questions and digging deep to ensure validity. Ensure content supports complex question resolution, not just surface-level explanations. - Design and provision foundational new-hire onboarding content as well as in-depth, role-specific learning experiences, working with the respective teams to design training solutions in relation to needs expressed, balancing adult learning styles to engage and use individual’s time effectively. - Develop structured learning paths that progress from conceptual understanding to practical application. Translate complex growth, operational, technical and industry knowledge into clear, consumable learning modules. - Curate content and training materials including but not limited to written guides, knowledge base articles, videos and quizzes both through our documentation repository as well as our Learning Management System (LMS), aligning elements of training to Process documentation as needed. - Translate nuanced business logic, edge cases and conditional workflows into formats optimised for AI understanding and retrieval. Curate and refine AI training content to improve reasoning, consistency and contextual awareness. - Ensure content remains accurate, relevant and aligned with evolving business needs over time. Maintain and regularly update materials to reflect product updates and system changes, ensuring both human teams and the AI Agent are effectively trained on changing standards. - Evaluate and report on training program effectiveness through knowledge assessments, surveys, feedback etc, leveraging findings to refine and enhance these programs, their related materials and improve training effectiveness. - Participate in change mgmt activities associated with implementing solutions that require training including coordination between teams, presentations/training, and similar. - Source external training content and courses as appropriate to assist with individual skills development across the Operations org. - Participation in the development of data tracking and KPIs to measure the progress and impact of large training initiatives and their evolution over time. Qualifications - 3+ years proven work experience within the Adult Learning, Training & Development discipline. - Strong experience building and launching training initiatives within RevOps/Sales, Operations and/or similar. - Strong experience designing learning content from scratch and iterating on existing materials. - Experience breaking down complex, ambiguous topics to create clear, structured documentation/training that supports advanced problem solving and decision making. - Experience working with AI Agents, chatbots, or knowledge-driven automation systems, familiarity with prompt design, knowledge structure and AI training methodologies. - Ability to work well with technical and non-technical team members at various levels, ranging from support to development. - Experience cultivating trust and building relationships interdepartmentally with cross-functional SMEs. - Excellent written/verbal communication and presentation skills. - Excellent organization, analytical, problem solving and critical thinking skills. - Familiarity with Jira, Postman, Salesforce and LMS systems considered an asset. - Automotive industry experience and/or familiarity with Dealership business models is a big plus! $88,000 - $95,000 a year Annual Performance bonus - 2,000 CAD What's in it for you: - We offer full training and a competitive total rewards package along with great benefits - Medical, Dental & Vision coverage - 100% premium coverage for employee / 50+% for dependents - Flexible work hours - Remote environment - Competitive pay - Visionary leadership team - Growth opportunities within a dynamic culture - Wellness & cultural initiatives (fitness challenges, wellness webinars, virtual games, regional activities, etc.) - Up to $1K per year for employee professional development - Stock options - we are all owners! Individual compensation decisions are based on a number of factors, including the candidate’s experience and qualifications and local market conditions. Please note, the foregoing salary range does not reflect an employee’s total compensation package, which may include bonus, company equity, and health benefits. AutoFi is an equal opportunity employer. Individuals seeking employment are considered without regards to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, sexual orientation, gender identity or other protected status under all applicable laws, regulations, and ordinances. Personal Information submitted as part of your application is subject to our website privacy policy, located at https://www.autofi.com/privacy-policy/