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Remote Hindi - AI Voice Data Collection Project

Company
Company Workable
Type
Opportunity Type Internship
Salary
Stipend / Salary Rs. 500 per hour, up to Rs. 1000 for 2 hours of recording
Location
Location Remote
Posted Date
Posted Date Today
Fluent Hindi clear pronunciation natural conversational ability basic computer literacy stable internet connection good-quality microphone/headset ability to follow instructions
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Open to all candidates with a minimum of a high school qualification; no specific degree, branch, or percentage requirement. Must be a native or near‑native Hindi speaker, possess a quiet home environment, stable internet, and a good‑quality microphone or headset. No backlogs or academic restrictions apply.

1
Round 1: Submit a 2‑minute voice audition
2
Round 2: Screening of audition for fluency, pronunciation, and voice quality
3
Round 3: NDA signing and final recording session (remote)
Arctic Engines is an enterprise‑grade AI human data operations company that specializes in creating high‑quality training data, reinforcement learning from human feedback (RLHF), and human‑in‑the‑loop pipelines for cutting‑edge AI models. The firm operates under the umbrella of the Apna Group, one of India’s fastest‑growing unicorns, backed by marquee investors such as Lightspeed, Tiger Global, Insight Partners, and Peak XV. With direct access to Apna’s massive workforce of over 60 million users, Arctic Engines can deliver massive volumes of clean, annotated data at speed, helping AI developers accelerate model development and improve performance. The role is a short‑term, remote voice‑data collection project aimed at building next‑generation conversational AI. Candidates will engage in natural, unscripted dialogues with an AI‑powered conversational bot across a variety of everyday and professional domains – travel, finance, retail, healthcare, telecom, education, and more. The objective is to capture authentic Hindi speech that reflects real‑world conversational patterns, tone, and nuance, which will be used to train speech‑to‑text, voice assistants, and dialogue systems. Key responsibilities include: 1. Record a 2‑minute audition sample in a quiet environment to demonstrate fluency and voice quality. 2. Upon selection, complete a total of 2 hours of conversational recording from home, split into multiple 10‑minute sessions. 3. Interact naturally with an AI bot, speaking for roughly 70 % of the conversation while the bot prompts topics. 4. Provide detailed, spontaneous, and context‑relevant responses without reading from a script. 5. Follow strict guidelines on pronunciation, clarity, and conversational flow. 6. Re‑record any segment that fails quality or technical checks. 7. Ensure a stable internet connection and use a good‑quality microphone or headset. 8. Sign a non‑disclosure agreement (NDA) before the final recording session. 9. Submit recordings within the stipulated timeline. 10. Maintain a quiet, controlled recording environment throughout the project. The tech stack is minimal – candidates need a computer or smartphone, a reliable internet connection, and a decent microphone. Recordings are captured via a web‑based interface that streams the AI bot and stores audio files securely. While the project is temporary, high‑performing contributors may be invited to future data‑collection initiatives, potentially progressing to senior annotator or quality‑control roles within Arctic Engines. Why join? This project offers a unique chance to contribute directly to the development of AI that will power voice assistants used by millions. Participants gain exposure to cutting‑edge AI workflows, earn competitive hourly compensation, and enjoy the flexibility of working from home. It is an ideal opportunity for freshers and students looking to build a portfolio in AI data operations while honing their communication skills.

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Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

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Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Fluent Hindi, clear pronunciation, natural conversational ability, basic computer literacy, stable internet connection, good-quality microphone/headset, ability to follow instructions) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Workable Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
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Model Answer: Implicit Wait sets a global timeout for all element lookups. Explicit Wait pauses execution until a specific ExpectedCondition (e.g. elementToBeClickable) is met. Fluent Wait allows defining polling frequency and ignoring specific exceptions like NoSuchElementException.
Explain the Page Object Model (POM) and its advantages in Test Automation. Answer ▼
Model Answer: POM is a design pattern that creates an object repository for web UI elements. It separates test scripts from page locators, reducing code duplication and making maintenance easy when UI elements change.
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Model Answer: Use dynamic XPath methods like contains(), starts-with(), text(), or XPath axes (ancestor, following-sibling, parent) instead of brittle absolute paths.
What is the difference between @BeforeMethod and @BeforeClass in TestNG? Answer ▼
Model Answer: @BeforeClass runs once before the first test method in the current class, while @BeforeMethod executes before each individual test method.
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Model Answer: In RestAssured: given().when().get('/endpoint').then().assertThat().statusCode(200).body('status', equalTo('ACTIVE')).
Why do you want to join Workable as a Remote Hindi - AI Voice Data Collection Project?
Preparation Tip: Highlight Workable's market reputation, recent tech innovations, and how your skills in Fluent Hindi directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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