Junior LLM Engineer

Salary range - ₹5-8 Lacs p.a + healthcare + ESOPs

Remote India based role

About ClearQuote

ClearQuote is a technology startup that builds AI-powered vehicle inspection solutions for rental and commercial fleets. The product uses computer vision on images/videos to automatically detect vehicle damage, generate condition reports and repair estimates, and track damage history over time

Role Description

We're looking for a junior LLM engineer to help build and scale intelligent language systems that transform raw inspection data into actionable insights for fleet managers worldwide.

What you will do

• Train, fine-tune, and evaluate LLMs for domain-specific tasks such as report generation, instruction following, summarization, and conversational workflows.
• Build data pipelines for text corpora: preprocessing, cleaning, prompt-engineering, synthetic-data generation, and quality checks.
• Experiment with modern LLM techniques: retrieval-augmented generation (RAG), tool-use, function-calling, multimodal LLMs, and agentic systems.
• Benchmark and iterate on model performance: latency, quality, hallucination control, safety, and consistency.
• Integrate LLM systems with product features: chat-based inspection assistant, automated reporting engine, multilingual support, and customer-facing tools.
• Stay updated with state-of-the-art model architectures (transformers, mixture-of-experts, RAG systems, multimodal LLMs, structured-output models).
• Ensure high code quality, enforce standards: clean, maintainable, documented code, tests, and reproducibility.

Qualifications

• B.E. / B.Tech / M.Tech or comparable degree in Computer Science, Electrical Engineering, AI/ML, Robotics, or related field.
• 1+ years experience in AI/ML with emphasis on Python and LLMs (can include meaningful internships)
• Solid understanding of transformer-based models (BERT, GPT-family, Llama, Mistral, etc.).
• Practical experience with deep-learning frameworks (PyTorch, TensorFlow) and libraries like HuggingFace Transformers.
• Familiarity with prompt engineering, fine-tuning methods (LoRA, PEFT), and evaluation techniques for LLMs.
• Understanding of NLP fundamentals: tokenization, embeddings, text classification, sequence modeling, QA, summarization.
• Good problem-solving skills, curiosity, and willingness to learn and iterate with ability to write clean, readable, and modular code.
• Familiarity with containerization, Docker, Git; comfort working in Linux/GPU environments; experience in deploying models to cloud or edge.
• Good communication skills to collaborate across teams (backend, product, operations) and mentor other engineers.

Why apply for this role

• Build LLM systems that power real-world automation in global automotive and fleet-tech workflows.
• Opportunity to be part of a small, fast-moving, and engineering-driven team with customers across markets.
• Flexible and dynamic startup environment, with potential to grow into technical leadership roles as the company scales.