Core Responsibilities

• Develop and optimize data pipelines using Python, Pandas, and Scikit-learn.
• Support prompt engineering initiatives for LLM-based applications.
• Conduct model evaluation and testing, ensuring accuracy, reliability, and performance.
• Work with Vector Databases (e.g., Pinecone, FAISS) for embedding storage and retrieval.

Secondary Responsibilities

• Assist in fine-tuning small and domain-specific models for production use.
• Write automation scripts for data preparation, cleaning, and feature engineering.
• Collaborate with product and engineering teams to enable API integrations for ML services.

Must-Have Skills

• Strong programming skills in Python with hands-on experience in Pandas and Scikit-learn.
• Basic knowledge of prompt engineering for LLMs.
• Understanding of model evaluation metrics and testing techniques.
• Exposure to Vector Databases (Pinecone, FAISS, or similar).

Good-to-Have Skills

• Experience with fine-tuning smaller ML or LLM models.
• Familiarity with REST APIs / GraphQL and integrating ML models into production workflows.
• Knowledge of data preprocessing pipelines and automation scripts.

Soft Skills

• Strong problem-solving and analytical mindset.
• Ability to work in a collaborative environment with cross-functional teams.
• Curiosity to explore and learn new AI/ML tools and frameworks.