Zensar Technologies: Opportunities in GEN AI Project (AV Annotation/Labeling & Prompt Engineering) 2025
Thanks for sharing this job posting! It looks like an exciting entry-level role in the growing field of generative AI (Gen AI), focused on data annotation, labeling (especially for audio-visual or AV data), and prompt engineering. Zensar Technologies, a leading digital transformation company with a strong emphasis on AI and ML services, is actively recruiting freshers and early-career professionals for this position. Based on recent updates, this off-campus drive is open to graduates from 2021–2026 batches, with roles potentially based in locations like Pune or Gurugram, and a flexible work mode (office or hybrid) determined post-interview.
Key Job Highlights
- Role Overview: You’ll support AI/ML initiatives by collecting, annotating, and labeling data (e.g., marking images, text, or AV content with precision using guidelines). This includes prompt engineering—crafting, refining, and testing prompts for AI models. It’s a tech-enabling position that involves collaboration with data scientists and project managers.
- Eligibility:
- Any graduate (e.g., BSc/BCS/BCA/BBA/BCOM/BA/MSc/MCS/MCA/MBA/MCOM/MA/BE/BTech, or equivalent).
- 10th: ≥35%, 12th/Diploma: ≥40%, Graduation: ≥40%.
- Strong problem-solving, communication, and eagerness to learn tools like annotation software.
- Salary: Starting at ₹3 LPA for freshers, adjustable based on interview performance.
- Other Perks: Potential for long-term growth in Zensar’s AI ecosystem, including exposure to enterprise AI solutions like their “AI Engineering Buddy” for legacy modernization and innovation.
- Application Process: Submit the form you provided (or the equivalent online link). No automatic data collection—provide details manually. Joining timeline: As soon as 7 days post-selection.
This aligns with Zensar’s broader push into Gen AI, where they’re building secure, compliant solutions for clients across industries, including multi-modal AI for text, images, audio, and video. It’s a great entry point if you’re passionate about AI ethics, data privacy, and hands-on ML support.
How to Apply
Use the Google Form-like fields you shared or apply directly via Zensar’s official channels:
- Direct Link (from recent postings): Apply Here
- LinkedIn Posting: View on LinkedIn
- Tip: Tailor your “Other Relevant Details” (Field 19) with any AI-related projects, certifications (e.g., in Python, Hugging Face, or basic ML), or why you’re excited about Gen AI.
Quick Tips for Success
- Prepare for Interviews: Expect questions on data labeling best practices, ethical AI (e.g., bias in annotations), and basic prompt engineering (e.g., “How would you refine a prompt for image captioning?”). Brush up on tools like LabelStudio or Prodigy.
- Stand Out: Highlight analytical skills—e.g., breaking down complex AV data tasks. If you’re a fresher, mention academic projects involving data handling.
- Timeline: Apply ASAP—these roles fill quickly, with recent announcements from September 21–23, 2025.
Since you’ve asked for interview tips again for the Zensar Technologies GEN AI Project (AV Annotation/Labeling & Prompt Engineering) role, I’ll provide a fresh, concise set of tips, focusing on new angles and avoiding repetition from my previous response. These tips are tailored to the role’s technical and behavioral expectations, emphasizing Zensar’s focus on proactive, detail-oriented candidates for their AI/ML initiatives. The interview process likely includes an HR screening, a technical round (data annotation or prompt engineering tasks), and a final discussion, typically spanning 2-3 rounds of 30-60 minutes each.
8 New Interview Tips for Zensar GEN AI Project Role
- Master the Basics of AV Annotation: Be ready to explain annotation types (e.g., bounding boxes, keypoint annotation for videos, or audio segmentation). Practice with a sample dataset (e.g., label a video frame for pedestrian detection). Say: “I’d ensure 90%+ accuracy by cross-checking labels against project guidelines.”
- Ace Prompt Engineering Questions: Expect tasks like: “Write a prompt to generate a concise audio transcript summary.” Practice crafting clear, specific prompts (e.g., “Summarize a 2-minute customer service call in 50 words, focusing on key issues”). Highlight iterative testing: “I’d refine prompts based on model output quality.”
- Showcase Ethical AI Awareness: Zensar emphasizes secure, compliant AI. Discuss how you’d handle bias in AV datasets (e.g., “I’d ensure diverse representation in labeled images to avoid skewed model training”). Mention GDPR or data privacy basics if relevant.
- Leverage Academic Projects: As a fresher, link coursework to the role. Example: “In my BCA project, I organized 200+ text entries for sentiment analysis, similar to structuring AV data.” Quantify impact (e.g., “Improved dataset accuracy by 15%”).
- Demonstrate Tool Familiarity: Mention exposure to annotation tools like LabelStudio, CVAT, or Prodigy, even if self-learned via YouTube tutorials. For prompt engineering, reference experimenting with platforms like Hugging Face or ChatGPT. Say: “I explored LabelStudio to tag images, adapting to project-specific schemas.”
- Prepare for Scenario-Based Questions: Expect: “How would you handle inconsistent annotations from a team?” Answer: “I’d propose a standardized guideline and regular quality checks, resolving discrepancies via consensus.” Practice 1-2 minute responses.
- Ask Strategic Questions: Show curiosity about Zensar’s AI ecosystem. Examples: “What specific AV annotation challenges do your clients face?” or “How does the team measure prompt effectiveness for Gen AI models?” This signals you’re thinking long-term.
- Polish Your Virtual Presence: For online interviews (likely via MS Teams), ensure a stable internet connection, quiet background, and professional attire (business casual). Keep a notepad for quick diagrams (e.g., annotation workflow). Practice a 30-second intro: “I’m [Name], a [Stream] graduate passionate about AI data quality.”
Bonus Tips
- Time Management: Zensar values quick joiners (7-15 days). Express flexibility: “I’m ready to start within a week to contribute to your AI projects.”
- Follow-Up: Post-interview, send a brief thank-you email: “Thank you for discussing AV annotation challenges. I’m excited about applying my data skills to Zensar’s Gen AI goals.”
- Quick Prep Resource: Try a free annotation demo on Roboflow or a prompt engineering course on Udemy (1-2 hours) to boost confidence.
