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AI-Native Product Manager – Graduate Trainee

Zycus

  • Bengaluru, India
  • 8 - 28 LPA
  • 0–1 Year
  • Posted Today
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About the role

Zycus is a global leader in enterprise procurement software, offering end‑to‑end Source‑to‑Pay (S2P) solutions that help Fortune‑1000 companies automate and optimise their spend management. With a strong focus on AI‑driven automation, Zycus has built the Merlin AI engine, a proprietary platform that injects intelligence into every stage of the procurement lifecycle – from sourcing and contract management to invoicing and analytics. The company’s headquarters are in Bengaluru, and it operates across more than 30 countries, serving a diverse portfolio of large enterprises.

Zycus prides itself on a culture of innovation, continuous learning, and a collaborative environment where engineers, product managers and data scientists work together to solve complex business problems.

The AI‑Native Product Manager – Graduate Trainee role is designed for fresh engineering graduates who are passionate about product management and artificial intelligence. As a trainee, you will work under senior product leaders, gaining hands‑on experience in market research, requirement gathering, and the creation of AI‑enhanced product features for enterprise SaaS applications. You will be exposed to the full product lifecycle – from discovery and definition to delivery and post‑launch analysis – while leveraging generative AI tools such as ChatGPT, Claude and Gemini to accelerate research, documentation and decision‑making.

Key responsibilities include

  • Using AI tools to conduct market research, competitive analysis and user‑research synthesis.
  • Assisting in the preparation of Product Requirement Documents (PRDs) and writing clear user stories with acceptance criteria.
  • Supporting product discovery through customer interviews, surveys and data analysis.
  • Collaborating with engineering, design and QA teams to ensure seamless feature delivery.
  • Tracking product metrics, analysing data and translating insights into actionable recommendations.
  • Learning and applying prompt‑engineering techniques to improve research productivity.
  • Exploring emerging AI technologies and proposing enhancements to product‑management workflows.
  • Contributing to the definition of AI‑driven features that leverage large language models and Agentic AI.
  • Participating in cross‑functional product reviews and brainstorming sessions.
  • Documenting findings, creating prototypes and presenting product concepts to stakeholders.

Tech Stack & Tools: The role will expose you to product‑management platforms such as Jira, Notion and Figma, data‑analysis tools like Excel and SQL, and AI services (LLMs, generative AI APIs). Basic understanding of REST APIs, SaaS architecture and cloud environments is beneficial.

Growth Path: Starting as a Graduate Trainee, high‑performing candidates can progress to Associate Product Manager, then Product Manager, and eventually Senior Product Manager or Product Lead, with opportunities to specialise in AI‑native product lines or move into product strategy roles.

Why Join Zycus? You will work on cutting‑edge AI‑powered enterprise products, learn from seasoned product leaders, and gain exposure to large‑scale SaaS deployments. The hybrid work model offers flexibility while keeping you connected to a vibrant tech hub in Bengaluru. Zycus invests heavily in employee development, providing mentorship, training programmes and a clear career ladder for aspiring product managers.

Eligibility & process

Who can apply

Bachelor of Engineering (B.E.) or Bachelor of Technology (B.Tech) in Computer Science, Information Technology or any other Engineering discipline. Must be a 2026 passout. Minimum 80% (or equivalent CGPA) in Class 10, Class 12 and undergraduate/postgraduate studies. No active backlogs and no history of backlogs/arrears. 0–1 year of work experience (freshers eligible).

Skills & stack

Product Thinking Market Research Competitive Analysis Ai Tool Usage Prompt Engineering Requirement Gathering User Story Creation Data Analysis

Selection process

  1. Round 1: Online aptitude & logical reasoning test
  2. Round 2: Technical/analytical interview focusing on product case studies and AI tool knowledge
  3. Round 3: HR interview assessing cultural fit and career aspirations

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
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 (Product Thinking, Market Research, Competitive Analysis, Ai Tool Usage, Prompt Engineering, Requirement Gathering, User Story Creation, Data Analysis) & 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 Zycus Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join Zycus as a AI-Native Product Manager – Graduate Trainee?
Highlight Zycus's market reputation, recent tech innovations, and how your skills in Product Thinking directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
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?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Zycus 8 - 28 LPA
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