Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Known financial firm, mid-level ML role with broad cloud/ML requirements yields moderate candidate competition.
Core LLM and ML skills transfer across industries, though GenAI finance focus requires some domain familiarity.
Explicit 3+ years applied ML requirement plus LLM, framework and cloud/MLOps mandates enforce strict filtering.
Design and implement experiments and fine-tuning workflows for large language models using Python and ML frameworks under senior guidance.
Support benchmarking and evaluation of LLMs focusing on performance analysis and anomaly detection.
Contribute to data curation, model training preparation, documentation, and participate in model review sessions to apply state-of-the-art evaluation methodologies.
Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or related technical field.
0–7 years overall IT experience with 3+ years in applied machine learning/data science including internships or academic research.
Proficiency in Python and hands-on experience with ML frameworks such as 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) and MLOps.
Early-career professionals with a focus on experimentation and applied research in LLMs within a collaborative team environment.
Candidates comfortable working under guidance but capable of independently handling data preprocessing and experiment documentation.
Practitioners with exposure to modern ML frameworks and cloud AI/ML infrastructure, aiming to contribute to frontier generative AI research.