N #2998
Applications open
Full-Time Job
Full Stack Genai Developer
Ascendion Engineering
About the role
Role Overview
We are looking for an experienced GenAI Engineer with strong hands-on expertise in Generative AI, Large Language Models (LLMs), RAG, and AI Agents. The ideal candidate should have experience building and deploying scalable, production-ready GenAI applications using AWS and Python. Frontend and backend development experience will be an added advantage but is not mandatory.
Key Responsibilities
- Design, develop, and maintain scalable GenAI applications using LLMs, RAG, and AI Agents.
- Integrate AI capabilities into customer-facing and employee-facing applications.
- Build and optimize GenAI solutions using AWS and cloud-native technologies.
- Develop and integrate APIs, microservices, and AI-powered application components.
- Implement security, governance, observability, and monitoring for AI-enabled solutions.
- Leverage AI-assisted development tools to improve software development efficiency.
- Collaborate with business and technology teams to deliver production-ready solutions.
Required Qualifications
- Bachelor's degree in Computer Science, Software Engineering, or a related field. 810 years of overall software development experience, with strong hands-on GenAI expertise.
- Strong proficiency in Python and experience with AWS services.
- Hands-on experience building applications using LLMs, RAG, and AI Agents.
- Experience with frameworks such as LangChain, LangGraph, or similar technologies.
- Understanding of APIs, microservices, CI/CD, testing, security, and software engineering best practices.
- Experience with AI-assisted development tools such as GitHub Copilot or Cognition Devin.
- Strong problem-solving and collaboration skills.
- Good to Have Experience with frontend technologies such as React, Angular, or Vue.
- Experience with backend development and cloud-native application architecture.
- Experience deploying and managing GenAI applications in production environments.
Eligibility & process
Who can apply
Experience: 7-10 Yrs. Open to matching qualifications.
Skills & stack
Selection process
- Online Assessment / Technical Interview Rounds / HR Review
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 Ascendion Engineering 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
Ascendion Engineering Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Ascendion Engineering 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 (Artificial Intelligence, Python, AWS, React, Full Stack, Stack, Aiml, Angular) & 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
Ascendion Engineering 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 Ascendion Engineering as a Full Stack Genai Developer?
Highlight Ascendion Engineering's market reputation, recent tech innovations, and how your skills in Artificial Intelligence directly solve their team's objectives.
Highlight Ascendion Engineering's market reputation, recent tech innovations, and how your skills in Artificial Intelligence 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 (Best in Industry)
Bengaluru, India
Experience: 7-10 Yrs. Open to matching quโฆ
