15 Oct
|
Enlighten Hacks - Community for Growth
|
Vadodara
15 Oct
Enlighten Hacks - Community for Growth
Vadodara
Job Title: Data Scientist – Language Models (AI/ML)
Location: Vadodara, Gujarat
Work Mode: Work from Office
Experience: 3+ Years
Department: AI/ML – Technology
Role Overview
We are looking for an experienced Data Scientist specializing in Language Models to take end-to-end ownership of AI/ML development. This role involves managing the complete lifecycle of language model projects, from raw data preparation and model training to evaluation, optimization, deployment, and production monitoring.
A key responsibility will be developing small language models (SLMs) for specific domain-focused use cases. This includes curating training datasets, selecting model architectures and training strategies, fine-tuning or pretraining models, applying distillation and quantization techniques, and establishing reliable evaluation frameworks.
As an early technical hire, you will play a significant role in building the AI/ML function, establishing development practices, and preparing the foundation for a growing team.
Key Responsibilities
• End-to-End Language Model Development
• Lead the complete development lifecycle of domain-specific small language models (SLMs), including data preparation, training, evaluation, and deployment.
• Select appropriate model architectures, training objectives, and alignment techniques such as Supervised Fine-Tuning (SFT), LoRA/QLoRA, RLHF, and DPO.
• Apply model distillation and quantization to reduce model size, inference costs, and latency while maintaining acceptable output quality.
• Deliver working, demonstrable models with measurable performance benchmarks and documented results.
• Data Preparation and Engineering
• Perform exploratory data analysis (EDA) and transform raw, unstructured text into clean, structured training datasets.
• Create instruction-tuning/SFT datasets and preference pairs for model alignment.
• Identify and resolve duplicate data, data leakage, formatting inconsistencies, tokenization issues, and context-length l
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