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Softobiz AI Engineer Intern 2026

Company
Company softobiz
Type
Opportunity Type Internship
Salary
Stipend / Salary Rs 15,000/month stipend
Location
Location Kochi, Kerala
Posted Date
Posted Date Today
Data Structures Algorithms Python 3.10+ Async Programming LangGraph Multi‑Agent Orchestration Prompt Engineering OpenAI Anthropic Azure OpenAI Vector Search Qdrant Azure AI Search Pydantic v2 JSON Schema Model Context Protocol (MCP) Temporal vLLM Git Clean Code Practices
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Aptitude Practice Questions Open Resource ↗
Helps candidates sharpen quantitative and logical reasoning needed for the initial screening round.
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Technical Interview Preparation Open Resource ↗
Covers core CS concepts, coding patterns, and system design topics relevant to AI engineering roles.
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Comprehensive Interview Guides Open Resource ↗
Provides curated interview experiences, sample questions, and tips specific to AI and software internships.
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Algorithm Practice Problems Open Resource ↗
Offers a large set of coding problems to improve DSA proficiency, essential for the coding test.

Pursuing or recently completed B.Tech/B.E./M.Tech/MCA in Computer Science or a related field; final‑year students or recent graduates; strong grasp of data structures, algorithms, and problem‑solving; hands‑on experience with Python 3.10+; no minimum percentage specified; no active backlogs allowed at the time of joining.

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Round 1: Online coding test (DSA & Python)
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Round 2: Technical interview (AI concepts, LangGraph, LLM integration, system design)
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Round 3: HR interview (fit, communication, career goals)
Softobiz is an emerging technology services firm headquartered in Kochi, Kerala, focused on delivering AI‑driven solutions to enterprises across India. The company blends deep domain expertise with cutting‑edge research in large language models, multi‑agent orchestration, and retrieval‑augmented generation. Over the past few years, Softobiz has built a reputation for rapid product delivery, a culture of continuous learning, and a collaborative environment where engineers are encouraged to experiment with the latest AI frameworks. The firm’s portfolio includes intelligent chat‑bots, automated document processing pipelines, and AI‑enhanced developer tools, all of which are deployed on cloud platforms such as Azure and AWS. The AI Engineer Intern role is a hands‑on, mentored internship designed to give fresh graduates real‑world exposure to production‑grade agentic AI systems. Interns will work side‑by‑side with senior AI engineers on building, testing, and scaling multi‑agent workflows using LangGraph and Python. The internship emphasizes strong fundamentals in data structures, algorithms, and clean Python code, while also introducing participants to advanced concepts like context engineering, model routing, and durable AI workflows. Successful interns may be offered a full‑time AI Engineer position based on performance and business needs. Key responsibilities include: 1. Design and implement multi‑agent pipelines with LangGraph, handling branching, loops, retries, and human‑in‑the‑loop checkpoints. 2. Develop context‑engineering modules to manage token budgeting, summarization, and layered context selection. 3. Integrate LLM providers (OpenAI, Anthropic, Azure OpenAI) and craft prompt‑engineering strategies. 4. Build retrieval pipelines using vector stores such as Qdrant and Azure AI Search, including embedding generation and relevance ranking. 5. Implement model‑routing logic to select optimal LLMs based on latency, cost, and quality. 6. Create evaluation and error‑analysis frameworks to measure reliability and guide iterative improvements. 7. Apply Pydantic v2 and JSON Schema for typed contracts and output validation. 8. Contribute to durable workflow implementations using Temporal for long‑running tasks. 9. Participate in code reviews, design discussions, and knowledge‑sharing sessions. 10. Document architecture, experiments, and findings in a clean GitHub repository. The tech stack spans Python 3.10+, async programming, LangGraph, Pydantic, vector databases, MCP, Temporal, and vLLM. Growth prospects include a clear path from intern to full‑time AI Engineer, exposure to end‑to‑end AI product development, and mentorship from industry experts. Joining Softobiz offers the chance to work on state‑of‑the‑art AI systems, gain production experience early in your career, and become part of a fast‑growing, innovation‑centric team.

softobiz — AI & Machine Learning Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

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.

Open Resource ↗
COMPANY GUIDE 🎯
softobiz Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for softobiz and off-campus tech roles.

Open Resource ↗
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.

Open Resource ↗
CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource ↗
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 (Data Structures, Algorithms, Python 3.10+, Async Programming, LangGraph, Multi‑Agent Orchestration, Prompt Engineering, OpenAI, Anthropic, Azure OpenAI, Vector Search, Qdrant, Azure AI Search, Pydantic v2, JSON Schema, Model Context Protocol (MCP), Temporal, vLLM, Git, Clean Code Practices) & 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
softobiz Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
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).
Why do you want to join softobiz as a Softobiz AI Engineer Intern 2026?
Preparation Tip: Highlight softobiz's market reputation, recent tech innovations, and how your skills in Data Structures directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: 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?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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