Data Scientist

Key Responsibilities

Research & Innovation

Stay updated with advancements in LLMs, transformer architectures, and multimodal systems.

Evaluate emerging AI techniques, frameworks, and APIs for business applications.

Explore real-time models, fine-tuning strategies, and inference optimization methods.

Solution Design & Prototyping

Design innovative solutions for complex AI challenges using advanced LLMs.

Develop proof-of-concepts (POCs) to validate technical feasibility.

Rapidly prototype applications using modern AI frameworks and orchestration tools.

Collaborate with cross-functional teams and subject matter experts.

Production Development & Engineering

Write clean, scalable, and maintainable production-level code.

Build robust agentic workflows using frameworks like LangGraph, CrewAI, or similar.

Implement advanced LLM capabilities such as tool calling, structured outputs, and multi-turn conversations.

Design scalable and fault-tolerant architectures for real-time AI applications.

Conduct code reviews and maintain high engineering standards.

Experimentation & Optimization

Design and execute experiments to evaluate model performance.

Develop evaluation frameworks for LLM outputs and user experience.

Optimize prompts, fine-tuning strategies, and inference performance.

Perform A/B testing, benchmarking, and statistical analysis.