Internship Python/ML development

Role Description

This is a full-time, on-site Internship role based in Hyderabad for Python/ML development. The intern will assist in designing, coding, and testing Python-based data processing scripts, machine learning components, and LLM-powered features used in Qstrat's analytics products. Day-to-day tasks include cleaning and transforming datasets, implementing and validating ML models, integrating LLMs into internal tools (retrieval-augmented pipelines, structured extraction, agentic workflows), writing modular and well-documented code, and supporting integration of algorithms into internal tools and applications. The role may also involve experimenting with new frameworks, optimizing model and LLM pipeline performance, and collaborating with data scientists, engineers, and product team members to refine solutions. Interns are expected to learn quickly, contribute to technical discussions, and follow best practices in version control, code review, and documentation.

This is a paid internship for a period of 4 months, with

competitive compensation above market standards for strong candidates

. We may offer full-time roles after the internship depending on performance and market conditions.

Qualifications

Strong foundation in Python programming, including writing clean, modular code and working with common libraries (e.g., pandas, NumPy).

Knowledge of machine learning concepts and exposure to ML frameworks such as scikit-learn, TensorFlow, or PyTorch.

Exposure to or strong interest in LLM integrations — working with LLM APIs (e.g., OpenAI, Anthropic), prompt engineering, retrieval-augmented generation (RAG), or agentic pipelines.

Basic understanding of data structures, algorithms, and computational complexity relevant to data processing and ML.

Familiarity with data handling and analysis, including working with structured datasets, data cleaning, and exploratory data analysis.

Experience with version control tools (e.g., Git) and collaborative development workflows is an advantage.

Ability to interpret technical requirements, document work clearly, and communicate effectively with cross-functional teams.

Currently pursuing or recently completed a degree in Computer Science, Data Science, Engineering, or a related quantitative field.

Interest in Private Markets, finance, or applied data science, and willingness to learn domain-specific concepts.