L #2711
Applications open
Full-Time Job
Artificial Intelligence Engineer
viamagus
About the role
About Viamagus Viamagus Technologies is a Bangalore-based product engineering company building technology solutions across industries, with AI adoption as a core strategic direction — not just in our products, but in how we build them. We're a lean team where engineers own their work end-to-end and work directly with leadership and clients.
About the Role This is a new role for a sharp fresher who lives and breathes AI. You will build AI agents — and then try hard to break them. You'll push them to their limits, find where they fail or slow down, and help fix it. You will write code every day and work directly with our senior engineers and CTO.
If you have built things with LLMs because you couldn't stop yourself, and you enjoy finding out exactly why something went wrong, we'd like to hear from you.
What You Will Do
- Build and experiment with AI agents and LLM-based workflows
- Stress-test AI systems: find failure points, wrong answers, edge cases and slow responses
- Measure performance (speed, cost, accuracy) and suggest improvements
- Write code in Python or JavaScript/TypeScript to build tools, tests and prototypes
- Try new AI models, tools and frameworks, and share what works with the team
- Explain your findings clearly, in writing and in conversation
What We Are Looking For (Must-Have)
- A recent graduate (B.Tech / B.E. / M.Tech / MCA or similar) with strong coding fundamentals
- Hands-on projects with LLMs or AI agents (for example LangChain, LangGraph, CrewAI, the OpenAI or Anthropic APIs, or MCP)
- Heavy daily use of AI tools, and curiosity about how they work and where they fail
- A "break it to make it better" mindset: you enjoy finding bugs and performance problems
- Sharp problem-solving and clear communication
Good to Have
- A GitHub profile or portfolio showing AI projects
- Hackathon wins, open-source contributions or research work in AI
- Basic knowledge of testing, APIs and the cloud
How to apply: Fill in the application form here: https://forms.gle/8P9ZqszbC8wnGEks8 (Please share your GitHub or project links — we look at what you've built more than your marks.)
Eligibility & process
Who can apply
Seniority: Entry level. Employment type: Full-time
Skills & stack
Selection process
Details are shared by the recruiter after shortlisting.
Prep kit
19 resourcesStudy material 4
Aptitude Practice Questions & Mock Tests
Curated logical, quantitative, and verbal reasoning problems for the initial online screening round.
Company-Specific Interview Preparation Corner
Detailed interview experiences, exam formats, and previous test questions for viamagus and tech roles.
Technical Placement Question Bank
High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides.
Algorithm, DSA & Live Code Debugger Practice
Hands-on problem sets to improve coding speed and step-by-step memory debugging.
Company-specific guides 4
APTITUDE & LOGIC
Aptitude Practice Questions & Online Mock Tests
Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.
COMPANY GUIDE
viamagus Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for viamagus and off-campus tech roles.
MOCK TESTS & PAPERS
Comprehensive Interview Prep Resources & Syllabus
Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.
CODING PRACTICE
Algorithm and Data Structure Problem Set
Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.
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 (Python, JavaScript, TypeScript, Git, Generative AI / LLMs, Data Structures & Algorithms, Python/Java, System Design) & 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
viamagus 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 viamagus as a Artificial Intelligence Engineer?
Highlight viamagus's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Highlight viamagus's market reputation, recent tech innovations, and how your skills in Python 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).
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.
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Competitive Package
India (Remote / Multiple), India
Seniority: Entry level. Employment type: …
