- Collaborate with product design and engineering teams to understand business and data needs.
- Develop, test, and deploy robust conversational agents that meet user needs and project requirements.
- Continuously improve the natural language understanding (NLU) and overall performance of conversational agents through customization and optimization techniques.
- Implement context-aware conversations and design conversation flows that handle complex user queries effectively.
- Develop and execute test plans to assess agent performance, identifying areas for improvement and iterating on data, prompts, and workflows.
- Communicate findings to stakeholders effectively.
- Stay current with industry developments and technical advancements.
- Collect, clean, and preprocess data to ensure quality and reliability.
- Explore and visualize data to identify patterns, trends, and relationships
- Train, evaluate, and deploy customized LLMs.
- Test and refine models to ensure their effectiveness.
- Monitor model performance and make necessary adjustments.
- Present results and suggestions through clear visuals and concise reports.
At least 5 years of professional experience as a Data Scientist or Engineer
Business level English verbal and writing.
A Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or a related field.
A minimum of two years experience as an ML engineer.
Proficiency in data manipulation and analysis tools like SQL and Pandas.
- Familiarity with Data Warehousing.
- Experience in developing, evaluating, deploying, running, and maintaining ML models.
- A strong understanding of AI agents, natural language processing, and machine learning concepts.
- At least 5 years of experience using LLMs and LLM-enabled programs in a professional, academic, or personal context, including exploring how they work and how to use them effectively.
- Experience with LangChain or other AI agent frameworks.
- Experience with LLM observability.
- Familiarity with the optimal use of LLMs, the tradeoff between fine-tuning and RAG, etc.
- Familiarity with prompt engineering best practices.
- Familiarity with data structures for ML and AI programs, including RAG and vector databases.
- Experience with building systems and infrastructure is preferred.
- Strong problem-solving and critical-thinking skills.
- Excellent communication and presentation skills.
TBD