Entry Level AI Jobs in India

Entry Level AI Jobs in India on Rex.zone focus on human-in-the-loop AI training workflows like data labeling, RLHF, prompt evaluation, and QA evaluation for large language models and multimodal systems. You will support LLM training pipelines by applying annotation guidelines, improving training data quality, and running model evaluation tasks across NLP, computer vision, and content safety labeling. This remote, full-time role works with structured annotation tools, quality checks, and calibration to drive model performance improvement for AI labs, tech startups, BPOs, and annotation vendors hiring through Rex.zone.

Job Heading: Entry Level AI Jobs in India

About the Role

You will contribute to AI/ML training and evaluation by labeling and reviewing datasets, executing RLHF and preference ranking, and performing prompt and response evaluation to improve large language model behavior. Work includes annotation guidelines compliance, edge-case documentation, rater calibration, inter-annotator agreement checks, and QA evaluation to ensure training data quality. You may handle NLP tasks such as named entity recognition, intent classification, and toxicity detection, as well as computer vision annotation such as bounding boxes, polygons, and segmentation. The day-to-day is remote and tool-driven, emphasizing precision, consistency, and measurable model performance improvement.

Key Responsibilities

Create and review labeled datasets for NLP, CV, and multimodal use cases; perform RLHF preference ranking and rubric-based scoring; run prompt evaluation and response quality checks for helpfulness, factuality, and safety; execute content safety labeling for policy, harassment, self-harm, and sensitive categories; follow annotation guidelines and record decision rationales for ambiguous cases; conduct QA evaluation using sampling plans, audits, and error taxonomy; participate in calibration sessions to reduce rater variance; track throughput and quality metrics that impact LLM training pipelines; collaborate with engineers and operations on task design, tooling feedback, and guideline iteration.

Required Qualifications

Strong written English and structured reasoning; ability to follow detailed annotation guidelines and apply consistent judgments; experience with spreadsheets, web tools, or labeling interfaces; familiarity with AI concepts such as supervised learning, model evaluation, and prompt/response patterns; comfort handling sensitive content as part of content safety labeling; attention to detail with an evidence-based approach to QA evaluation; ability to work independently in a remote environment with reliable connectivity and schedule discipline.

Preferred Qualifications

Exposure to RLHF workflows, pairwise ranking, or rubric-based evaluation; experience with named entity recognition, text classification, or conversational AI testing; experience with computer vision annotation (bounding boxes, polygons, segmentation); understanding of inter-annotator agreement, error analysis, and training data quality practices; familiarity with prompt evaluation for LLMs and common failure modes like hallucinations and policy violations; prior work with AI labs, tech startups, BPOs, or annotation vendors.

Tools and Workflows You Will Use

Annotation platforms and labeling tools; QA workflows including sampling, audits, and escalation; calibration and feedback loops for rater alignment; prompt evaluation templates and evaluation rubrics; dataset versioning practices and guideline change logs; basic analytics for throughput, accuracy, and disagreement tracking; secure remote work practices for handling project data.

Remote Work and Employment Details

Remote Type remains Remote and the role is FULL_TIME. Work is coordinated through Rex.zone and supports distributed teams across different time zones. You will receive task queues, quality targets, and written guidelines; performance is measured through training data quality, annotation guidelines compliance, and QA evaluation outcomes.

How to Apply on Rex.zone

Visit Rex.zone and search for Entry Level AI Jobs in India to find the active listing and application steps. Prepare a concise resume highlighting data labeling, RLHF, prompt evaluation, QA evaluation, and any NLP or computer vision annotation experience. Include examples of guideline-driven work, quality processes you followed, and how you improved training data quality or reduced errors.

Frequently Asked Questions

Q: What does an Entry Level AI role typically do in AI training workflows?
You support LLM training pipelines by completing data labeling, RLHF preference ranking, prompt evaluation, and QA evaluation tasks that improve training data quality and model performance improvement.
Q: Is this role remote and full-time?
Yes. Remote Type is Remote and Employment Type is FULL_TIME, with work delivered through online tooling and written annotation guidelines.
Q: Why does the page say India but the metadata lists Country as US?
The page targets the keyword “entry level ai jobs india,” while the job metadata defaults provided for Country remain US and must stay unchanged per the posting requirements.
Q: Do I need prior experience with RLHF or LLM evaluation?
Not always, but familiarity with RLHF, prompt evaluation, and rubric-based scoring is helpful. Strong guideline adherence and consistent QA evaluation performance are core requirements.
Q: What domains might I work on?
Common domains include NLP (named entity recognition, text classification), computer vision annotation (bounding boxes, segmentation), and content safety labeling for policy and risk categories.
Q: What skills should I highlight to rank for this job category?
Emphasize AI data labeling, RLHF, prompt evaluation, LLM evaluation, QA evaluation, annotation guidelines compliance, training data quality, named entity recognition, computer vision annotation, and content safety labeling.
Q: What kinds of employers hire for these roles?
AI labs, tech startups, BPOs, and annotation vendors commonly hire for these workflows, including remote full-time and contract-style project work found through Rex.zone.
Q: How is quality measured?
Quality is measured through audits, disagreement rates, rubric adherence, annotation guidelines compliance, error taxonomy trends, and overall training data quality that impacts model evaluation outcomes.

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