Applied AI Researcher (LLMs, Agents & Machine Learning)
Salary: $7,000 – $8,500 per month
About the role
We are a research and technology organization focused on developing advanced analytical solutions that combine data science, machine learning, and artificial intelligence.
Key responsibilities
Analyze structured and unstructured data to identify patterns, trends, and statistically significant insights that support research and business objectives.
Develop and apply predictive models, including regression, classification, time-series forecasting, and other statistical or mathematical modeling techniques.
Conduct exploratory data analysis (EDA), feature engineering, data quality assessment, and model validation using appropriate evaluation frameworks.
Design and execute statistical experiments, including hypothesis testing, significance testing, and power analysis.
Research, design, and develop AI agents powered by Large Language Models (LLMs) to automate and orchestrate analytical workflows.
Integrate AI agents with databases, predictive models, and data pipelines to enable autonomous querying, reasoning, and interpretation of results.
Evaluate agent performance across task completion, reasoning quality, tool utilization, hallucination rates, and failure handling.
Build scalable data processing, machine learning, and visualization solutions using Python and related technologies.
Query, manipulate, and analyze data from relational databases using SQL.
Contribute to reproducible data pipelines, experiment tracking, code reviews, technical documentation, and research reporting.
About you
Bachelor's, Master's, or PhD in Computer Science, Data Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative discipline.
Proficiency in Python and core data science libraries, including Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, and Seaborn.
Strong understanding of data wrangling, feature engineering, data normalization, missing data handling, and outlier detection.
Solid mathematical foundations in linear algebra, calculus, optimization, probability.