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REQUIREMENT_ID_1564 β€’ 3-DAY_ACTIVE_POLICY

AI Agent Engineer Intern

Company
Company Lexsi Labs
Type
Opportunity Type Internship
Salary
Stipend / Salary Rs 15,000/month
Location
Location Mumbai, Remote
Posted Date
Posted Date Today
Python Large Language Models Agent Frameworks Evaluation Pipelines Enterprise Data Integration Cloud Platforms Git Docker Research Methodology Problem Solving
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Aptitude and Logical Reasoning Practice Open Resource β†—
Helps sharpen analytical thinking and problem‑solving skills essential for designing agentic systems.
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Company Interview Preparation Guide Open Resource β†—
Provides insights into typical interview questions and formats used by tech companies.
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Lexsi Labs Internship Insights Open Resource β†—
Offers specific information about the internship structure, expectations, and projects at Lexsi Labs.
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Coding Challenges and Algorithms Open Resource β†—
Builds strong coding fundamentals and algorithmic thinking needed for technical interviews.

Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field; minimum 70% (or equivalent CGPA 8.0+) in the qualifying year; no backlogs; eligible for internship programs; strong programming background in Python; interest in AI agents, evaluation, alignment, or enterprise AI.

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Round 1: Technical interview covering coding and AI concepts
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Round 2: Technical + product fit interview focusing on agent design and evaluation
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Round 3: HR interview
Lexsi Labs is a frontier AI laboratory dedicated to building aligned, interpretable, and safe AI systems for real‑world deployment. The organization’s research portfolio spans agentic AI, alignment, interpretability, evaluation systems, enterprise AI, and foundational model research across structured, tabular, and proprietary data. Transparency, auditability, robustness, and controllability are treated as first‑class constraints, ensuring that every system is not only powerful but also trustworthy. The AI Agent Engineer Intern role invites candidates to tackle high‑impact agentic AI problems across applied, research, and product domains. Interns will not be confined to building small internal bots; instead, they will work on real agentic systems, evaluation infrastructure, enterprise use cases, research prototypes, and product tooling that can shape how AI agents are built, tested, aligned, and deployed. Depending on interests, interns may contribute to one or more of three tracks: Applied Agentic AI, Agent R&D and Evaluation, or Product and Tooling Agents. Key responsibilities include: 1. Designing and prototyping enterprise workflow agents that reason over complex tasks. 2. Building evaluation frameworks to benchmark tool use, planning, and long‑horizon reasoning. 3. Developing harnesses for new agent architectures and studying failure modes. 4. Creating product‑grade tooling that improves researcher and engineer productivity. 5. Integrating LLMs with structured and proprietary data sources. 6. Collaborating with research, engineering, and product teams to iterate on prototypes. 7. Documenting findings, experiments, and design decisions. 8. Contributing to research papers, technical reports, or internal knowledge bases. 9. Maintaining clean, version‑controlled codebases. 10. Participating in code reviews and knowledge sharing sessions. The technical stack revolves around Python, large language models, agent frameworks such as LangChain or custom harnesses, evaluation pipelines, cloud services (AWS/GCP), Git, Docker, and data connectors for enterprise systems. Strong programming skills, a passion for agents, and a willingness to tackle ambiguous problems are essential. Growth opportunities are abundant: interns can progress to research engineers, product engineers, or research scientists, publish papers, contribute to open‑source projects, and lead cross‑functional initiatives. The fast‑moving, research‑driven environment encourages ownership, rapid prototyping, and the translation of ideas into production‑grade components. Why join Lexsi Labs? The lab offers a chance to work at the frontier of agentic AI, collaborate with world‑class researchers, and build systems that have tangible real‑world impact. Flexible remote work, part‑time scheduling, and a culture that values substance over polish make this internship an ideal launchpad for aspiring AI professionals.

Lexsi Labs β€” AI & Machine Learning Interview Guide

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

APTITUDE & LOGIC πŸ“–
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 β†—
COMPANY GUIDE 🎯
Lexsi Labs Interview Preparation Corner

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

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

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

Open Resource β†—
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, Large Language Models, Agent Frameworks, Evaluation Pipelines, Enterprise Data Integration, Cloud Platforms, Git, Docker, Research Methodology, Problem Solving) & 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
Lexsi Labs 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 β–Ό
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 β–Ό
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 β–Ό
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 β–Ό
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 Lexsi Labs as a AI Agent Engineer Intern?
Preparation Tip: Highlight Lexsi Labs'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.
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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