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.