AI Data Annotator / Labeling QA
Also posted as: Data Annotator · Data Labeling Specialist · AI Trainer · AI Tutor · LLM Response Evaluator · Annotation QA · Content Quality Analyst (AI)
An AI Data Annotator produces and checks the human data that AI models learn from: labelling images, video, audio and text against a written guideline, and increasingly rating, ranking and red-teaming what a model wrote. In India the hiring now splits three ways — AI labs and enterprises (Amazon's AGI Data Services, Innodata, iMerit, TELUS Digital, Uber AI Solutions), global platforms paying by the hour (Outlier, Mercor, Turing, Handshake AI), and a large layer of Indian annotation vendors and BPOs (Macgence, Shaip, Fidel Softech, G-Labs) — and 26 of 34 job postings we analysed are open to 0-2 years with many accepting any graduate, so it is the most reachable AI job on this list. Be clear-eyed about the pay: posted monthly salaries run Rs 15,000-30,000 (about 1.8-3.6 LPA) and Indian-language platform rates start near $2/hour, so treat this as a paid entry point that teaches you rubrics, quality metrics and model failure modes — the ladder out runs through prompt, eval and annotation-QA roles, not through staying an annotator.
- Across 68 AI Data Annotator / Labeling QA job postings in India, the most-requested capabilities are Label data accurately against guidelines (94%), Audit label quality and compute agreement (78%) and Annotate and evaluate in an Indian language (59%).
- Pay at 0–2 yrs averages about ₹2.5 LPA (verified across 2 salary sites: AmbitionBox, Glassdoor); most earn ₹1.4–2.4 LPA (base pay); employers offer ₹2.5–3.5 LPA (median of 17 job postings that state pay, 0–2 yrs · Naukri, other sites, Indeed, LinkedIn).
- At least 48 open roles in India — the dated job postings for this role we read in the last 60 days; portals count a title's exact phrase, which undercounts a job posted under many titles, checked 03-10-2026.
- Hiring is concentrated in Remote (India), Bengaluru and Hyderabad.
- Postings read from LinkedIn 50%, other portals 24%, Indeed 15% and company career pages 12%.
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Capabilities employers ask for
How often employers ask for each capability, measured across the job descriptions behind this page. Click one to see what "knowing it" means, how to learn it, and how to prove it.
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Label data accurately against guidelines
Nearly every posting is the same sentence in different words: apply a written guideline consistently, hit a quality target, and flag what the guideline does not cover instead of guessing. Vraify wants you to 'follow structured annotation guidelines, perform quality checks, flag unclear data'; Concentrix sets 'productivity and quality targets, defined per project'; Cognizant asks you to adhere 'to rigorous protocols'; HumanBit wants you following 'detailed annotation guidelines' in English. This is the whole job at entry level, so you must be able to label to a spec accurately and know when to escalate.
Explore 3 practice tools →Audit label quality and compute agreement
Many seats ask you to check work, not only produce it, and QA is the usual route from annotator to lead. TELUS Digital hires 'Quality Assurance Raters' in English and Hindi; Outlier wants experience reviewing calls and audio 'QA, call auditing, content moderation'; Jobgether's team lead maintains 'guideline adherence, taxonomy management, and quality assurance processes'; Rhombus Power tracks 'productivity benchmarks, and quality assurance processes'. You must be able to sample someone else's labels, find the error pattern behind them, and measure agreement between annotators.
Explore 3 practice tools →Annotate and evaluate in an Indian language
A named language is a hiring gate across much of this market, from English fluency to a specific dialect. Outlier wants you to 'read, write and evaluate text fluently in Tamil' and hires native Hindi voice evaluators; Shaip wants a 'native speaker of Hindi – Haryanvi dialect'; OWOW transcribes Hindi-English phone audio with 'code-switching' captured; Amazon asks for near-native fluency in English. A common language with nothing else is the floor of this market; a scarce dialect, or a language paired with a real skill, is what lifts you off it.
Explore 3 practice tools →Evaluate AI outputs as a domain expert
Seats that gate on a specific expertise, rather than general carefulness, are the clearest pay signal in this market. scanO hires dental image annotators; Globik AI wants radiologists to review CT and MRI studies; Renan Partners builds medical datasets and doctor-patient dialogue; Triomics wants clinical abstractors who 'identify systematic error patterns in AI outputs'. One verifiable, named skill attached to your annotation work is worth more than any amount of extra labelling speed.
Explore 3 practice tools →Evaluate and rank model responses (RLHF / preference data)
A large part of this market is judging model output rather than producing raw labels. TELUS Digital assesses 'AI-generated responses and search engine results for accuracy, relevance, usefulness'; Handshake AI has you 'evaluating LLM responses'; Glean runs 'the human evaluation system behind Glean's AI product quality'; Zyno AI wants you reporting 'poor-quality AI responses instead of giving unnecessarily generous ratings'. You must be able to compare responses against a rubric, rate them honestly and explain the rating in a sentence.
Explore 3 practice tools →Write labeling guidelines and evaluation rubrics
A smaller set of postings want you writing the rules, not just applying them. Turing has you 'create automated scoring rubrics evaluating logical coherence and rule adherence'; Glean wants you to 'follow and improve labeling guidelines and rubrics'; Hire Feed evaluates work 'against domain-specific quality rubrics'; HackerKernel lists 'rubrics and annotation guidelines' outright. Clear written English that turns a fuzzy quality judgement into a rule someone else can apply is the separator between labelling and rubric or QA-lead work.
Explore 3 practice tools →Build an LLM evaluation harness
Few annotation postings want a real evaluation harness, but the eval and quality-analyst titles are built on it. Brewcode wants you to 'run evaluation sets, review AI outputs, curate ground truth'; Glean validates LLM-as-a-judge against human labels; Objectways wants experience in 'model evaluation'; Turing names 'ML evaluation frameworks'. Build a small golden set and score model output against it repeatably before you interview for anything with 'evaluation' in the title.
Explore 4 practice tools →Explain how LLMs work and where they fail
Some postings expect you to know how models behave and fail, and those are the AI-trainer and quality seats rather than plain labelling. Glean wants you to 'validate LLM-as-a-judge outputs against human labels'; Brewcode wants evaluation of 'LLM-based agents' with RAG and tool calling; Turing asks for 'familiarity with LLM behavior, prompt design'; Accenture's Trust & Safety analyst interrogates models on responsible-AI topics. Knowing what a hallucination, a tool call and a context window are is the cheapest step from labelling to AI-trainer work.
Explore 4 practice tools →Write production-quality Python for AI work
Python is a minority ask here and should not stop you applying, but it shows up in the better seats. Objectways wants 'Python or SQL at a working level'; Brewcode lists Python and SQL beside LLM evaluation; Hire Feed's AI trainer role wants Python, Java or JavaScript 'for scripting and data generation tasks'; Ringg AI wants a basic grasp of QA and test cases. Enough scripting to check label files, count disagreements and reformat JSONL is the cheapest lever out of hourly labelling work.
Explore 4 practice tools →Work in annotation tools (Label Studio / CVAT / Labelbox)
Fewer postings name a tool than you might expect, but the ones that do agree on the family. Hire Feed requires 'video annotation tools such as CVAT, Labelbox'; QualityAI asks for CVAT; Chitrakala Interactive wants Label Studio; Objectways lists CVAT, Label Studio, Encord and SageMaker Ground Truth. Learn CVAT or Label Studio properly, including boxes, segmentation, keypoints, tracking, hotkeys and export formats, and any other tool is a day of reading.
Explore 3 practice tools →Design and version prompts systematically
Only a handful of postings ask you to author prompts rather than judge answers, and they sit in the AI-trainer end of the market. Handshake AI has you 'developing domain-specific prompts'; Accenture's Trust & Safety role asks you to 'develop and execute prompt strategies to interrogate large language models'; Outlier and Turing list prompt engineering and prompt design. You should be able to write a prompt that deliberately probes a model's weak spot, then judge what came back.
Explore 4 practice tools →Apply responsible-AI and data-protection basics
No posting in this sample makes responsible-AI or data-protection policy the job; the nearest asks are concrete and operational. OWOW wants you to 'identify and redact personal information' per its PII handling rules; Accenture's Trust & Safety analyst wants 'deep familiarity and passion for responsible AI'; Quik Hire Staffing asks for familiarity 'with data privacy regulations and ethical AI practices'. Learn what counts as PII and how to redact it, plus the basics of consent, since that is what audio and medical annotation actually requires.
Explore 3 practice tools →Families: Annotation, quality & human feedback · Evaluation, safety & observability · Product, business & communication · Programming foundations · LLM application development
"AWS" on a JD is not "learn AWS"
The words employers write, translated into what they want you to be able to do for this role.
Skip, for now
- Deep learning / training models in PyTorch — Deep learning appears in 1 of 34 job postings (AMETEK), and even there you prepare and validate datasets for ML engineers rather than train anything. Understand what a model does with your labels; do not spend months on backprop.
- Fine-tuning and RLHF implementation — Fine-tuning or RLHF is named in 3 of 34 job postings, always describing the data you produce (preference pairs, corrected transcripts), never code you write. Learn what a preference pair is and why ranking quality matters — that is the whole requirement.
- Paid 'data annotation certification' courses — Not one of the 34 job postings asks for a certification. Entry is gated either on tests you cannot buy — Innodata runs a 2-hour assessment with a 75% pass mark and disqualifies AI tool use, Amazon and TELUS run graded language and judgement tests — or on nothing at all: Shaip, G-Labs and Handshake AI each say no prior experience is needed and training will be provided. A public labelled dataset with a written guideline beats any certificate in both cases.
- Agent frameworks (LangChain, LangGraph, CrewAI) — Agent tooling shows up in 2 of 34 job postings and only as the thing being evaluated, not built — Mercor red-teams conversational agents, Handshake AI has you assisting frontier models with web navigation. Skip it until you are actually moving into a GenAI engineering track.
- Specialised tooling (GIS, CAD, remote sensing) — Geospatial, CAD and remote-sensing tooling is needed in only 2 of 34 job postings (ARDEM, Energy Aspects). Specialist gates do pay — Vraify wants 1+ year of Blender at Rs 670-957/hour, scanO wants a BDS — but they pay because you already have the skill, not because you cross-trained for the posting. Learn one general annotation tool (CVAT or Label Studio) properly first; pick up the domain tool only if you take that specific job.
A public annotation project with a written guideline, a measured agreement score, and an LLM rating set
Pick a domain you genuinely know — a language you speak natively, cricket clips, medical leaflets, code, local street imagery — and collect 300 public items. Write a 2-page labelling guideline with definitions, positive and negative examples, and a decision rule for the three ambiguous cases you will inevitably hit, then label all 300 in Label Studio or CVAT and export the dataset. Get one friend to independently label 50 of the same items, compute Cohen's kappa, find where you disagreed, and revise the guideline — that revision is the artefact hiring managers care about. Finally take 40 model answers to prompts in the same domain, score them against a 5-point rubric you wrote, rank them pairwise, and write a one-paragraph rationale for each, because that is exactly what TELUS, Innodata and Uber AI Solutions will test you on.
Start from 300 public items you collect yourself in a domain you genuinely know — a language you speak natively, match clips, product leaflets, street imagery
- Public repo with the guideline (v1 and v2), the exported dataset in a standard format, and a README explaining what changed between versions and why
- Inter-annotator agreement reported as a number (Cohen's kappa or percent agreement) on the 50 double-labelled items, with the top three disagreement causes named
- 40 rated model outputs with a written 5-point rubric, pairwise rankings, and a one-paragraph rationale per item in clean English (or your target language)
- A short error taxonomy — the 5 mistake types you saw most, with counts — plus your own throughput and accuracy numbers tracked in a sheet, the way an employer's KPI dashboard will track you
What the interviews look like
The rounds you'll actually face, in the order they usually come.
- 1
Screening / eligibility
Recruiter checks graduation status, language fluency (Hindi, English, Tamil and Urdu come up constantly — Amazon and Canva require native-level Hindi, Shaip wants a native Haryanvi speaker from interior Haryana), residency for rater roles (TELUS requires 5 years in India), shift willingness, and your equipment: ARDEM specifies an i5 laptop, 8GB RAM, Full HD monitor and 100+ Mbps internet.
- 2
Timed annotation / rating assessment
An unsupervised graded test on the actual task. Innodata runs a 2-hour online assessment with a 75% pass mark and disqualifies candidates who use AI tools. Expect to label or rate real items under a guideline you were handed 10 minutes earlier, with accuracy and speed both scored.
- 3
Calibration and rationale review
You defend your labels against a reviewer's. They will hand you deliberately ambiguous items and check whether you apply the guideline, escalate, or guess — and whether your written rationale is specific and readable. Practise saying 'the guideline does not cover this, here is what I would escalate and why'.
- 4
Domain / operations fit
For domain tracks (Uber AI Solutions healthcare and commerce, Coffeee.io code evaluation, Energy Aspects geospatial, Mercor red-teaming) a subject-matter interview on your actual background. Everyone also gets the operations conversation: throughput targets (Samsara: 350+ events a day), shift patterns including nights, contract length, and confidentiality — Amazon frames customer privacy as its most important tenet.
What people ask before choosing this role
Can a fresher get an AI Data Annotator / Labeling QA job in India?
Yes, this is one of the more reachable AI-era roles. 40 of the 68 job postings behind this page accept 0–2 years of experience. The rest want more, so expect the fresher-friendly openings to be competitive.
What is the salary of an AI Data Annotator / Labeling QA in India?
Pay at 0–2 yrs averages about ₹2.5 LPA (verified across 2 salary sites: AmbitionBox, Glassdoor); most earn ₹1.4–2.4 LPA (base pay); employers offer ₹2.5–3.5 LPA (median of 17 job postings that state pay, 0–2 yrs · Naukri, other sites, Indeed, LinkedIn). Not every posting states pay, and pay varies widely by city and by whether the employer is an IT-services firm, a global capability centre or a product startup.
How long does it take to become an AI Data Annotator / Labeling QA?
The six capabilities employers ask for most add up to roughly 49 focused hours — about 7 weeks at 8 hours a week, if you are starting from zero on all of them. Most people are not: the self-check on this page works out what you can skip, which is usually a large part of it.
What skills do you need for an AI Data Annotator / Labeling QA role?
Across the 68 job postings behind this page, the most-requested capabilities are Label data accurately against guidelines (94% of postings), Audit label quality and compute agreement (78% of postings) and Annotate and evaluate in an Indian language (59% of postings). Note these are capabilities, not tools — employers write tool names, but what they are buying is the ability to do the work.
Which cities in India post the most AI Data Annotator jobs?
Remote (India) (35), Bengaluru (9), Hyderabad (6) and Delhi NCR (5) — counted across the 68 job postings behind this page. Remote-India roles are counted separately where the posting said so.
Is demand for AI Data Annotator roles in India growing?
The work is moving from labelling images to checking AI output: Ringg AI now hires 'AI Agent QC' staff from data-labelling or call-centre QA backgrounds, and Objectways' lead role asks for tool-call traces and inter-annotator agreement design. Entry pay has not followed yet — AmbitionBox shows ₹2.5-2.8 L a year at both 0-1 and 1-3 years across 1,400+ salaries, with average pay up 11% over two years — so the better-paid seats go to people with a credential, such as Cogito Tech's ₹40,000-50,000 a month for clinically trained data taggers.
Do I need a degree or a paid certificate for this?
Nothing on this page requires a paid certificate, and none of the 68 job postings behind it asked for one by name. What they ask for is evidence you can do the work — a public repo, a deployed project, something a hiring manager can open. That is what the path on this page is built to produce.
Companies with this role open in India
A sample of employers we saw hiring for this role — IT services, global capability centres, product companies and startups. Each links to one of the company's postings for this role, checked open on 04-10-2026; where none is open, to its current openings instead.
What it pays
most earn ₹1.4–2.4 LPA (base pay) · verified across 2 salary sites: AmbitionBox, Glassdoor
Employers offer ₹2.5–3.5 LPA: median of 17 job postings that state pay, 0–2 yrs · Naukri, other sites, Indeed, LinkedIn
AmbitionBox only, not cross-checked yet
Not enough salary data yet for Senior · 5+ yrs.
Across all levels: the middle half earns ₹1.4–2.5 LPA (base pay) · Glassdoor
How much demand
What each job portal shows for this role's title — the readings behind the openings figure above.
- ≈ 7estimated: 5 of 18 inspected results carry the title · checked 04-10-2026glassdoor
- 1616 of 20 results, read in full, carry the title · checked 04-10-2026naukri
- at least 3737 of the first 1204 results carry the title (reading stopped: cap) · checked 04-10-2026linkedin
- 1313 of 20 results, read in full, carry the title · checked 04-10-2026indeed
- 48dated job postings for this role we read in the last 60 days — the figure above, because no portal's count reached it
Where this role is heading
- The work is moving from labelling images to checking AI output: Ringg AI now hires 'AI Agent QC' staff from data-labelling or call-centre QA backgrounds, and Objectways' lead role asks for tool-call traces and inter-annotator agreement design.
- Entry pay has not followed yet — AmbitionBox shows ₹2.5-2.8 L a year at both 0-1 and 1-3 years across 1,400+ salaries, with average pay up 11% over two years — so the better-paid seats go to people with a credential, such as Cogito Tech's ₹40,000-50,000 a month for clinically trained data taggers.
Capability percentages come from 68 job descriptions read in full on 03-10-2026. How we do this