Solid Data - Senior GenAI Applied Researcher

Solid is revolutionizing the data analytics landscape with its AI Agents Enablement platform, designed to

dramatically reduce response times and enhance data-driven decision-making.

We are developing an AI Powered Analytics Management Platform, which standardizes business analytic

processes, automates them and empowers analysts with information and recommendations that

increase their productivity and analysis quality. It also supports and enables smart AI based bots and

agents that rely on Solid’s automated semantic layer to answer analytics questions.

We are looking for a senior GenAI applied researcher who will join our team and play a crucial role in

researching and implementing GenAI-based and ML-based components in Solid’s platform.

Responsibilities:

● Conduct cutting-edge research in the fields of GenAI, ML and search optimization.

● Design, develop, deploy and evaluate LLM-based tools and AI agents.

● Innovate by proposing new ideas, approaches, and strategies for leveraging AI within the startup's product roadmap.

● Collaborate with other researchers, ML and software engineers to integrate these methods into the platform's architecture effectively.

● Optimize models and RAGs for scalability, performance, and efficiency.

● Stay updated with the latest advancements and publications in ML and GenAI to inform and improve our approaches.

● Communicate technical concepts and findings effectively to non-technical stakeholders.

● Mentor junior team members and foster a collaborative environment conducive to knowledge

sharing and growth.

Qualifications:

● At least 5 years of practical experience in designing, implementing, and deploying machine learning models.

● Proficiency in classic ML, deep learning and NLP, with a strong understanding of algorithms, optimization methods, and model evaluation.

● Minimum 1 year of hands-on experience specifically in generative AI, including LLM integration, prompt engineering, chain-of-thought techniques and RAGs implementation.

● Proficiency in Python and packages commonly used by data scientists such as Pandas, Numpy, Scikit-Learn, TensorFlow etc.

● Strong verbal and written communication skills in English, to articulate complex technical concepts to non-technical stakeholders effectively.

● Cultural fit with the team, with a fast paced, open and collaborative team environment.

● Experience in SQL, BI, data analytics, data governance, or other data stack components is a significant advantage.