 Design, develop, and deploy machine learning models and data science
solutions

 Work on LLM-based use cases such as prompt engineering, evaluation, finetuning, and model optimization

 Build and optimize pipelines for structured and unstructured data

 Develop and evaluate applications using LLMs (e.g., text generation,
classification, summarization, entity extraction)

 Conduct prompt engineering and experimentation to improve model outputs

 Support model benchmarking, testing, and performance evaluation

 Analyze large datasets to extract actionable insights and patterns

 Perform exploratory data analysis (EDA) and feature engineering

 Build data visualizations and reports for stakeholders

 Participate in client-facing technical discussions and presentations

 Translate business requirements into AI/ML solutions and solution architectures

 Provide consultative insights based on project learnings and industry trends

 Work closely with cross-functional teams (engineering, QA, annotation, delivery
leads)

 Support end-to-end project lifecycle:



requirement gathering → development →
deployment → monitoring

 Ensure quality, scalability, and reliability of AI deliverables

Requirements
 2–6+ years of experience in data science, machine learning, or AI solutions

 Experience working on AI/LLM or NLP-related projects is highly preferred

 Exposure to client-facing or delivery environments is a solid advantage

 Strong proficiency in Python (NumPy, Pandas, Scikit-learn)

 Experience with ML frameworks (TensorFlow, PyTorch)

 Hands-on experience with LLMs / NLP tools (e.g., Hugging Face, OpenAI APIs,
embeddings, RAG frameworks)

 Support evaluation and benchmarking methodologies used to determine dataset
quality and model performance.

 Work with Subject Matter Experts, Quality teams, and Technical Product
Managers to investigate potential data or Ground Truth issues.

 Solid understanding of:

o Machine learning algorithms

o Data preproces

📌 Research Data Scientist-AI/LLM Delivery (India)
🏢 MarketScope
📍 India