GEN AI – Annotation / Labeling / Prompt Engineering (Freshers)

Zensar's interview process typically starts with an online aptitude test, followed by a technical interview that probes your understanding of data annotation tools, basic AI concepts, and problem‑solving ability. The final HR round assesses cultural fit and communication skills. Candidates report a moderate difficulty level; preparation should focus on aptitude, Python basics, and AI fundamentals.

Zensar promotes a collaborative, learning‑centric culture where employees are encouraged to experiment and upskill. The company offers flexible work arrangements, regular tech talks, and a clear focus on work‑life balance, making it a supportive environment for fresh talent.

Bachelor's or Master's degree in Computer Science, Information Technology, Electronics & Communication, or related discipline; minimum 60% aggregate (or CGPA 6.0/10); graduating batch 2023‑2026; no active backlogs at the time of joining; strong written and verbal communication skills; basic understanding of AI/ML concepts is a plus.

Round 1: Online aptitude & logical reasoning test → Round 2: Technical interview covering data annotation tools, basic AI concepts, and problem‑solving → Round 3: HR interview focusing on fit, communication, and career aspirations

Zensar Technologies, a flagship digital solutions and technology services company of the RPG Group, is headquartered in Pune, India, and operates across the USA, UK, South Africa, and Mexico. With a strong focus on innovation, Zensar helps enterprises accelerate their digital transformation journeys through cloud, analytics, AI, and automation. The company’s culture blends global best practices with an entrepreneurial spirit, encouraging employees to experiment, learn, and deliver high‑impact solutions for Fortune‑500 clients. Over the past decade, Zensar has built a reputation for investing in talent, offering continuous up‑skilling programs, and fostering a collaborative environment where fresh ideas are welcomed. The role of GEN AI – Annotation / Labeling / Prompt Engineering is designed for fresh graduates eager to dive into the fast‑growing generative AI ecosystem. As a member of the AI data team, you will be responsible for preparing high‑quality training data, crafting effective prompts, and supporting model fine‑tuning activities. This position offers a unique blend of hands‑on data work and exposure to cutting‑edge AI tools, making it an ideal launchpad for a career in artificial intelligence. Key responsibilities include: 1. Annotate and label image, text, and audio datasets using tools such as Labelbox, CVAT, or custom in‑house platforms. 2. Develop and refine prompts for large language models (LLMs) to generate desired outputs. 3. Perform quality checks and ensure consistency across annotated datasets. 4. Collaborate with data scientists and AI engineers to understand labeling requirements and provide feedback. 5. Maintain documentation of annotation guidelines, versioning, and data provenance. 6. Assist in creating synthetic data pipelines to augment limited datasets. 7. Track and report annotation metrics (accuracy, inter‑annotator agreement, turnaround time). 8. Stay updated with emerging AI annotation techniques and suggest process improvements. 9. Participate in internal hackathons and knowledge‑sharing sessions. 10. Support the deployment of prompt‑engineered solutions in pilot projects. Tech stack: Python, Pandas, NumPy, OpenAI API / Anthropic Claude, Labelbox, CVAT, Git, JIRA, basic Linux commands. Growth path: Junior Annotation Engineer → Senior Annotation Engineer → AI Data Specialist → AI Project Lead → AI Solutions Architect. Why join Zensar? You will work on real‑world generative AI projects for global clients, receive mentorship from seasoned AI professionals, and benefit from Zensar’s robust learning ecosystem (Zensar Academy, certifications, and internal mobility). The role offers a competitive CTC of 3‑6 LPA, exposure to the latest AI tools, and a clear career trajectory in a company known for its employee‑first policies.