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JD #3315
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Junior Developer – AI

Larsentoubrocareers

  • Chennai, India
  • 3.5–4.5 LPA
  • 2 Years
  • Posted Yesterday
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About the role

Larsen & Toubro (L&T) is one of India’s largest multinational conglomerates, operating across engineering, construction, manufacturing, technology and financial services. With a legacy of over 80 years, L&T has built a reputation for delivering complex, large‑scale projects and pioneering innovations in sectors such as infrastructure, energy, defense and digital solutions. The company’s technology arm focuses on cutting‑edge research in artificial intelligence, Internet of Things, and cloud platforms, aiming to embed intelligence into traditional engineering workflows and create new product offerings.

The role of Junior Developer – AI is designed for fresh engineering graduates who are passionate about building AI‑driven products. Reporting to the AI Solutions Lead, the candidate will work within a cross‑functional team that includes data scientists, product managers and senior engineers. The primary focus will be on developing and integrating generative AI capabilities, building prototypes, and delivering production‑ready services that leverage large language models (LLMs) and natural language processing (NLP) techniques.

Key Responsibilities

  • Design, develop and integrate AI/ML models and generative AI features into L&T’s product suite.
  • Build AI‑powered applications using LLMs, implementing prompt engineering and retrieval‑augmented generation (RAG).
  • Work with embeddings and vector databases (e.g., Pinecone, Milvus) to enable semantic search and knowledge retrieval.
  • Develop and expose RESTful APIs for AI services, ensuring scalability and security.
  • Create prototypes and proof‑of‑concepts to validate new AI ideas and present them to stakeholders.
  • Support model testing, deployment pipelines, and debugging in cloud environments.
  • Collaborate with engineering and product teams to translate business requirements into technical solutions.
  • Maintain code quality using Git/GitHub, conduct code reviews and write documentation.
  • Stay updated with the latest advancements in generative AI, LLMs, and related tooling.
  • Participate in knowledge‑sharing sessions and contribute to the AI community within L&T.

Tech Stack: Python, PyTorch/TensorFlow, Scikit‑learn, LangChain, LlamaIndex, SQL, Git, REST APIs, vector databases, cloud platforms (AWS/Azure).

Growth Path: Starting as a Junior AI Developer, high performers can progress to AI Engineer, Senior AI Engineer, and eventually AI Solution Architect or Product Lead, with opportunities to lead large‑scale AI initiatives across L&T’s business verticals.

Why Join L&T? L&T offers a stable, well‑structured career environment with exposure to real‑world, high‑impact projects. Freshers get mentorship from industry veterans, access to state‑of‑the‑art AI labs, and a clear roadmap for technical and managerial growth. The company’s commitment to innovation, strong brand reputation, and competitive compensation make it an attractive launchpad for a career in AI.

Eligibility & process

Who can apply

B.Tech degree in Computer Science, Information Technology, Electronics & Communication, Electrical Engineering or related streams with a minimum of 60% aggregate (or CGPA 6.0/10). Candidates from the 2025, 2026 or 2027 graduating batches are eligible. No active backlogs at the time of joining. Freshers and candidates with up to 2 years of relevant experience are welcome.

Skills & stack

Python AI Machine Learning Generative AI Large Language Models Prompt Engineering Retrieval‑Augmented Generation Embeddings

Selection process

  1. Round 1: Online aptitude & coding test
  2. Round 2: Technical interview (AI/ML concepts, coding, system design)
  3. Round 3: HR interview (fitment, communication, career goals)

Prep kit

19 resources
Study material 4
Company-specific guides 4
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, AI, Machine Learning, Generative AI, Large Language Models, Prompt Engineering, Retrieval‑Augmented Generation, Embeddings) & 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 Larsentoubrocareers 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 Larsentoubrocareers as a Junior Developer – AI?
Highlight Larsentoubrocareers'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).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Larsentoubrocareers 3.5–4.5 LPA
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