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.