[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-ai-training-jobs-in-india":3},{"Ques":4,"Slug":22,"Header":23,"job_category":36},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19],{"A":8,"Q":9},"AI training jobs in India typically involve creating and validating training data used to build AI\u002FML models. Common tasks include data labeling, RLHF preference ranking, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling to improve training data quality and model performance.","What are AI training jobs in India?",{"A":11,"Q":12},"Yes. The role is explicitly Remote and FULL_TIME, aligned with the provided job metadata.","Is this role remote and full-time?",{"A":14,"Q":15},"RLHF (Reinforcement Learning from Human Feedback) uses human judgments to steer model behavior. In this job, you may perform preference comparisons, rubric-based ratings, and consistency checks that create high-signal feedback data for LLM alignment and large language model evaluation.","What is RLHF and how does it relate to this job?",{"A":17,"Q":18},"Core skills include AI training workflows, data labeling, annotation guidelines compliance, training data quality practices, QA evaluation, LLM evaluation, prompt evaluation, and domain-specific skills such as named entity recognition, computer vision annotation, and content safety labeling.","What skills are most important for AI training work?",{"A":20,"Q":21},"AI labs, technology companies, tech startups, BPOs, and annotation vendors use AI training operations to scale LLM training pipelines, improve evaluation coverage, and maintain data quality.","What kinds of employers use this type of work?","ai-training-jobs-in-india",{"desc":24,"title":25,"content":26},"AI training jobs in India focus on building and improving AI\u002FML systems by creating high-quality training data, running RLHF workflows, performing LLM evaluation, and enforcing annotation guidelines compliance. On Rex.zone, you will support real AI training pipelines used by AI labs, tech startups, and annotation vendors by labeling data, reviewing model outputs, and driving model performance improvement through training data quality and QA evaluation. This remote full-time role includes prompt evaluation, content safety labeling, and structured feedback that helps large language model evaluation and safety tuning at scale.","AI Training Jobs in India",[27,30,33],{"h2":28,"desc":29},"AI Training Jobs in India — Remote AI Training Specialist","Title: AI Training Specialist\nDate: 25-02-2026\nCompany: Rexzone\nCountry: US\nRemote Type: Remote\nEmployment Type: FULL_TIME\nExperience Level: Mid-Senior\nIndustry: Technology\nJob Function: Engineering\nSkills: AI training, RLHF, data labeling, LLM evaluation, prompt evaluation, QA evaluation, training data quality, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR\n\nYou will execute and improve AI training workflows across NLP and computer vision datasets, producing reliable labeled data and evaluation signals that directly impact model behavior. Work includes RLHF preference ranking, rubric-based QA evaluation, prompt\u002Fresponse evaluation, and error analysis to identify systematic failure modes and improve model performance.\n\nKey Responsibilities:\n- Produce labeled datasets for NLP tasks such as named entity recognition, text classification, summarization, and instruction-following.\n- Perform RLHF tasks: preference comparisons, rationale capture (when required), and consistency checks using calibrated rubrics.\n- Run LLM evaluation and prompt evaluation for accuracy, helpfulness, harmlessness, and policy compliance.\n- Conduct QA evaluation, audits, and adjudication to improve training data quality and reduce label noise.\n- Apply annotation guidelines compliance, document edge cases, and propose clarifications to annotation playbooks.\n- Support computer vision annotation including bounding boxes, polygons, segmentation, and keypoints when projects require CV labeling.\n- Perform content safety labeling across categories such as hate, harassment, self-harm, sexual content, and violent content, aligned to policy.\n- Collaborate with project leads to track inter-annotator agreement, drift, throughput, and quality metrics.\n\nWhat You’ll Work With:\n- Large-scale LLM training pipelines, evaluation harnesses, and annotation platforms\n- Taxonomies, rubrics, golden sets, and calibration rounds\n- Dataset versioning, sampling strategies, and error buckets for model performance improvement\n\nQualifications:\n- Experience in data labeling, QA evaluation, or LLM evaluation in production or high-throughput environments\n- Strong written English for prompt evaluation and rubric-based judgment\n- Familiarity with NLP concepts (NER, classification) and\u002For computer vision annotation\n- Ability to follow precise guidelines, maintain consistency, and handle sensitive content for content safety labeling\n\nHow to Apply:\n- Apply via Rex.zone and include a short summary of your annotation, RLHF, or evaluation experience and the domains you’ve worked in (NLP, CV, content safety).",{"h2":31,"desc":32},"What Makes These AI Training Jobs Different on Rex.zone","These AI training jobs in India are structured around production-grade AI\u002FML training workflows rather than generic tagging. Your work is tied to measurable training data quality, annotation guidelines compliance, and model performance improvement. Projects may span LLM training, RLHF preference data, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling for AI labs, tech startups, BPOs, and annotation vendors.",{"h2":34,"desc":35},"Typical Workstreams and Modifiers","This page covers common search modifiers and project types so candidates can quickly match intent: remote and full-time (this role), plus adjacent hiring patterns including contract, freelance, entry-level, and senior roles on similar programs. Domain coverage includes NLP, computer vision, content safety, and large language model evaluation across LLM training pipelines.","AI Data Operations"]