Data Scientist - II
lever
Job Description
- Research & Innovation - Stay current with the latest LLM research, architectures, and advancements in the field including real-time models and multimodal systems. Evaluate emerging techniques and methodologies for potential application to business problems. Monitor developments in transformer architectures, fine-tuning approaches, model optimization, and real-time inference. Research and assess new LLM capabilities, frameworks, and API features as they emerge
- Solution Design & Prototyping - Identify and define approaches for complex AI challenges leveraging state-of-the-art LLMs. Design and build proof-of-concept solutions to validate technical feasibility. Rapidly prototype LLM-based applications using modern frameworks and orchestration tools. Conduct rigorous experiments to evaluate different approaches and methodologies. Work collaboratively in multi-disciplinary team environments and establish professional networks with subject matter experts
- Production Development & Software Engineering - Write clean, maintainable, production-quality code following software engineering best practices and design patterns. Develop robust, scalable agentic workflows using orchestration frameworks (such as LangGraph, CrewAI, or similar). Implement advanced LLM features, including tool calling, function calling, structured outputs, and multi-turn conversations. Build production-grade systems utilizing Model Context Protocol (MCP) and other emerging standards. Design and implement scalable, fault-tolerant architectures for real-time LLM-powered applications. Conduct thorough code reviews and maintain high code quality standards. Optimize code for performance, memory efficiency, and cost-effectiveness in production environments
- Experimentation & Optimization - Design rigorous experiments to test hypotheses and validate model performance. Develop evaluation frameworks for LLM outputs, system performance, and user experience. Optimize prompt engineering strategies, fine-tuning approaches, and inference efficiency. Conduct A/B tests, performance benchmarking, and statistical analysis