Back
Strong employer brand, mid-level ML role, Bengaluru metro, and generic Data Scientist title increase competition.
LLM fine-tuning and ML engineering skills are broadly transferable across industries.
Explicit 3+ years applied ML requirement plus LLM fine-tuning, PyTorch, cloud, and MLOps skills required.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and implement experiments and fine-tuning workflows for large language models (LLMs) using Python and ML frameworks.
Support benchmarking and evaluation of LLMs, including performance analysis and anomaly detection.
Assist in data preparation and document experiment results and methodologies for internal knowledge sharing.
Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or related technical field.
0–7 years of overall IT experience; at least 3 years in applied machine learning or data science (including internships or academic research).
Proficiency in Python and hands-on experience with ML frameworks like PyTorch or TensorFlow.
Basic understanding of LLM architectures, fine-tuning techniques, evaluation metrics for generative models, and familiarity with cloud AI/ML services (AWS, Azure, or GCP).
Early-career professional with solid practical exposure to machine learning workflows, especially related to generative AI models and fine-tuning.
Comfortable working under guidance, contributing to experimentation and learning in a research environment.
Familiar with Python-based model training pipelines and interested in staying current with advances in generative AI and MLOps practices.