Staff Data Scientist

Essential functions

• Python

• Prompt engineering

• Must have 2+ working experience on Gen AI (LLM, RAG, LoRA, Deep learning etc. )

• Best practices for prompt engineering

• How LLM can be used in applications for a variety of tasks

• NLP

• Understanding of typical NLP problems: classification, NER, summarization, question answering, sentiment analysis, etc.

• Theoretical intuitive understanding of how Transformers work (tokenization, attention, etc).

• Word and sentence embeddings

• Vector search

• Vector databases, performance tuning

• Document chunking techniques

• LLM applications development

• LangChain, LlamaIndex

• Chain of Thoughts, DSP, and other techniques

• Agents and tools

• Google cloud (GCP)

Qualifications

• Preferable, the engineers are expected to have IT services/consulting experience.

• Proficient in developing LLM-powered systems using advanced prompt engineering techniques, RAG and agentic design patterns. Experienced with frameworks like LangChain, LlamaIndex, and DSPy.

• Familiar with evaluation approaches and metrics for different types of LLM-based systems.

• Experienced with keyword and vector search methods, including understanding of their underlying algorithms. Familiar with popular vector search engines.

• Competent in various document understanding models and techniques to parse complex documents and implement effective chunking strategies for RAG systems.

• Familiar with LLM and embedding models fine-tuning techniques.

• Competent in using joint vision-language and generative models to solve various problems related to image generation, visual question answering, and multi-modal search. Familiar with diffusion models and associated techniques like LoRA, Dreambooth, and ControlNet.

• Understanding of the challenges and risks associated with the development of Generative AI systems and how to mitigate them.

• Familiar with various architecture design patterns for different types of LLM-based applications such as chatbots, text2sql, document understanding, etc. Familiar with various approaches to scalability and cost reduction in Generative AI systems.

• Ability to stay updated with the latest advancements in Generative AI and integrate emerging technologies to drive innovation and improve the performance of AI systems.

• Familiar with Responsible AI principles and Human-AI interaction design best practices.

Would be a plus

• Proficient in developing LLM-powered systems using advanced prompt engineering techniques, RAG and agentic design patterns. Experienced with frameworks like LangChain, LlamaIndex, and DSPy.
• Familiar with evaluation approaches and metrics for different types of LLM-based systems.
• Experienced with keyword and vector search methods, including understanding of their underlying algorithms. Familiar with popular vector search engines.
• Competent in various document understanding models and techniques to parse complex documents and implement effective chunking strategies for RAG systems.
• Familiar with LLM and embedding models fine-tuning techniques.
• Competent in using joint vision-language and generative models to solve various problems related to image generation, visual question answering, and multi-modal search. Familiar with diffusion models and associated techniques like LoRA, Dreambooth, and ControlNet.

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office