AI Engineer

Genrate AI is an early stage startup based in Israel with a mission to accelerate B2B sales with generative AI. Through personalized messaging, entity snapshots, insightful information, and sophisticated internal and external data integration, we turn every salesperson into a master communicator (For more on the product please check genrate.ai ).

Role Description

This is a hybrid LLM Engineer role located in Dubai, with the possibility for remote work. As an LLM Engineer, you will be responsible for designing and implementing production ready and scalable agentic AI architectures, systems, and workflows, external data integrations, as well as traditional Machine Learning and Statistical models. In this role,

1. You will be a central driver in influencing the architecture of our multi-agent infrastructure.
2. You will get to work with all the latest SOTA in LLM models, frameworks, and techniques.
3. You will have ownership of critical features which will visibly impact our product.

Qualifications

1. Complete fluency in English.
2. Strong production ready experience in Python and Git.
3. Experience with LLM and Agent frameworks (such as LangChain, Langgraph, LlamaIndex).
4. Experience in prompt engineering, best practices, chain-of thought, multi-shot prompting, etc.
5. Strong experience with API integration and async frameworks.
6. Strong active statistics and probability theory knowledge.
7. Good understanding of machine learning concepts, algorithms, and techniques.
8. Experience with frameworks for rigorous testing and validation of models and prompts.
9. Experience with knowledge-graphs and vector-databases in production projects.
10. Experience in GoLang.
11. Previous experience at an early-stage startup.
12. Experience working with the latest open/closed source LLMs (OpenAI, LLaMA, etc) in production.
13. Production experience of multi-agent systems (react frameworks).
14. Experience fine-tuning LLM models.
15. Experience with synthetic data creation, testing, and validation pipelines.
16. Experience developing agentic RAG systems and working with Vector Databases (Weaviate preferably).
17. Strong theoretical background to be able to implement new publications to production.

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