JD #2438
Applications open
Internship
AI-Native Software Engineer β Trainee
Spektrasystems
About the role
Spektra Systems LLC is an emerging technology firm focused on building AIβdriven solutions for enterprises worldwide. Though headquartered in the United States, the company operates a fully distributed team with a strong presence in India, enabling it to tap into the country's vast pool of tech talent. Spektra prides itself on a culture of rapid innovation, where engineers are encouraged to experiment with generative AI tools, contribute to openβsource projects, and deliver productionβgrade software that solves real business problems. The organizationβs remoteβfirst policy ensures that employees enjoy flexibility while staying connected through regular virtual syncβups, mentorship programs, and collaborative code reviews.
The role of AIβNative Software Engineer β Trainee is designed specifically for fresh graduates and students who are eager to dive into fullβstack development powered by modern AI coding assistants. As a trainee, you will work under the guidance of senior engineers on live projects that integrate large language models, AI agents, and promptβengineering techniques into web applications. This handsβon exposure will help you build a robust portfolio while learning best practices in cloud deployment, CI/CD pipelines, and agile development.
Key Responsibilities:
1. Develop and maintain frontβend components using React.js, Next.js or Angular.
2. Write clean, modular backβend services in Python, JavaScript/TypeScript or C#.
3. Design, implement, and consume RESTful APIs and interact with SQL databases.
4. Leverage AI coding assistants such as Claude Code, Cursor, Codex or GitHub Copilot to accelerate development and improve code quality.
5. Participate in code reviews, unit testing, and debugging sessions.
6. Collaborate with product and design teams to translate requirements into technical specifications.
7. Deploy applications on cloud platforms and set up CI/CD workflows.
8. Contribute to documentation, knowledgeβbase articles, and internal tooling.
9. Continuously explore emerging AI tools and propose enhancements to the development workflow.
10. Attend mentorship sessions and weekly tech talks to upskill in AIβaugmented software engineering.
Tech Stack: JavaScript, TypeScript, Python, C#, React.js, Next.js, Angular, REST APIs, SQL, Git, GitHub, Claude Code, Cursor, Codex, GitHub Copilot, AI Agents, Prompt Engineering.
Growth Path: Starting as a trainee, high performers can progress to Junior Engineer, then to FullβStack Engineer, and eventually to AI Solutions Architect or Team Lead within 2β3 years, depending on skill acquisition and project impact.
Why Join Spektra? You will work on cuttingβedge AIβpowered projects from day one, receive mentorship from industry veterans, enjoy a flexible remote work environment, and gain exposure to the latest generative AI tools that are reshaping software development. This role offers a solid foundation for a longβterm career in AIβenhanced engineering.
Eligibility & process
Who can apply
Any graduate degree (B.Tech, B.E., B.Sc., MCA, BCA) from any stream; minimum 60% aggregate marks; batch years 2022β2026; no active backlogs at the time of joining; must have a genuine interest in AIβaugmented development and basic programming knowledge.
Skills & stack
Selection process
- Round 1: Online Aptitude Test
- Round 2: Technical Interview (coding, AI tool usage, system design)
- Round 3: HR Interview
Prep kit
19 resourcesStudy material 4
Aptitude Practice Questions
Curated aptitude problems to sharpen quantitative and logical reasoning for the online test at Spektra Systems.
Company Interview Preparation Guide
Comprehensive guide covering typical interview formats and sample questions for tech roles like AIβNative Engineer.
Comprehensive Interview Prep Resource
Allβinβone resource for coding, system design, and softβskill preparation tailored for freshers applying at Spektra.
Algorithm & Data Structure Problem Set
Extensive problem set to practice coding challenges commonly asked in technical rounds for fullβstack positions.
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
Spektrasystems Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Spektrasystems 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 (JavaScript, TypeScript, Python, C#, React, Next.js, Angular, REST APIs) & 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
Spektrasystems 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 Spektrasystems as a AI-Native Software Engineer β Trainee?
Highlight Spektrasystems's market reputation, recent tech innovations, and how your skills in JavaScript directly solve their team's objectives.
Highlight Spektrasystems's market reputation, recent tech innovations, and how your skills in JavaScript 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.
6 LPA
Internship
Remote, India
Nov 08, 2026
