Company DescriptionJanooma is India's first agent-driven AI marketplace, enabling seamless buyer-seller interactions by leveraging an innovative conversational layer. With a database of over 4 million businesses across India, Janooma delivers live quotes from verified vendors to buyers and connects service providers with high-intent leads through WhatsApp. This platform facilitates everything from electronics to home services in one conversation, providing convenience and efficiency for all users. By solving the cold-start problem before its launch, Janooma is poised to revolutionize the marketplace with upcoming expansions to 10 Indian cities by 2026 and Southeast Asia by 2027.LLM Systems EngineerLocation: Bengaluru (Hybrid)Experience: 0-1 year (Freshers from 2024-2026 batches are strongly encouraged)We are building next-generation large language model infrastructure focused on advanced model composition, optimization, and continuous capability enhancement across multiple domains and categories.About the RoleYou will play a foundational role in designing and implementing modular, scalable architectures for working with large language models. This includes building automated systems for model discovery, intelligent model merging/composition, architecture parsing, performance evaluation, gap analysis, and iterative improvement pipelines that power multiple specialized LLMs.You will work on end-to-end LLM engineering workflows — from low-level model introspection to high-level automated training and deployment loops — enabling the creation and continuous evolution of high-performance models across various categories and use cases.Key ResponsibilitiesDesign and implement automated pipelines for discovering, evaluating, and selecting high-performing open-source LLMs for different categoriesDevelop advanced model composition techniques (union-style capability aggregation and intersection-style conservative merging) using state-of-the-art merging frameworksParse and introspect LLM architectures in detail (similar to DOM tree parsing) — working with layers, attention mechanisms, state dictionaries, and parameter structuresBuild and maintain iterative improvement loops: task processing, multi-model comparison, automated evaluation, knowledge gap detection, synthetic data generation, and targeted fine-tuning/mergingImplement efficient fine-tuning workflows using parameter-efficient methods (LoRA/QLoRA, PEFT) and delta adapter techniquesCreate robust evaluation frameworks using multiple metrics (ROUGE, BERTScore, faithfulness, hallucination detection, etc.) and LLM-as-Judge systemsOptimize inference pipelines for high throughput using tools like vLLM or TGISet up MLOps practices for model versioning, experiment tracking, documentation, and deployment to Hugging Face HubContribute to building reusable, modular components that can support multiple LLM categories and future domain expansionsRequirements (Freshers Welcome!)B.Tech / M.Tech in Computer Science, Artificial Intelligence, Machine Learning, or equivalent (2024–2026 batch)Strong proficiency in Python and PyTorchHands-on experience with Hugging Face Transformers libraryPractical experience fine-tuning LLMs (Llama-3, Qwen2, Phi-3, or similar) using PEFT/LoRA/QLoRASolid understanding of Transformer architecture (attention mechanisms, layers, embeddings, LayerNorm, MLP blocks, etc.)Comfort with model introspection tools (named_modules, state_dict, named_parameters)Good knowledge of Git, Linux, and basic MLOps workflowsStrong problem-solving skills and excitement about working at the systems level of large language modelsBig Advantages (Not Mandatory)Exposure to model merging frameworks (mergekit or similar) and techniques like DARE, TIES, or SLERPExperience with synthetic data generation or distillation methodsFamiliarity with preference optimization (DPO, ORPO, etc.)Experience running large models with vLLM or Text Generation Inference (TGI)Active Hugging Face profile with public fine-tunes or experimentsUnderstanding of evaluation benchmarks and LLM-as-Judge patternsWhat We OfferDirect ownership of critical components in a cutting-edge LLM engineering stackAccess to high-end GPU cloud resources (A100/H100)Fast learning curve and rapid growth — strong performers can move into senior LLM Architect roles quicklyOpportunity to work on multiple LLM categories and advanced model evolution systemsCollaborative environment with clear architecture guidance and mentorshipThis is a high-impact role where you will gain deep, production-grade expertise in the most advanced areas of LLM engineering in 2026.How to ApplyPlease send your resume + GitHub profile link + a brief note or link showcasing one relevant LLM project (fine-tuning, merging, evaluation pipeline, or inference work) to Subject Line: LLM Systems Engineer – 2026We review applications on a rolling basis.
Job Title
LLM Engineer