Generative AI Data Scientist & ML Engineer

What is the opportunity?

Are you passionate about pushing the boundaries of artificial intelligence? Do you thrive in environments where innovation and cutting-edge research are at the forefront? We are offering an exciting opportunity to join our team as a Data Scientist / Researcher specializing in Generative AI Models. In this role, you will develop and optimise and use Large Language Models (LLM's), to solve complex problems and drive innovation in our AI solutions.

Job Description

As a researcher in this role, you will focus on the technical implementation and optimization of large language models (LLMs) to bring autonomous AI agents to life within a corporate setting. Your responsibilities will include:

• Fine-Tune LLMs: Customize and optimize pre-trained LLMs for specific corporate applications, ensuring high performance and accuracy in various NLP tasks such as text classification, summarization, and entity recognition.

• Vector Search Methodology: Develop and implement advanced vector search techniques, including the creation of efficient indexing and retrieval systems, to enhance the organization's ability to quickly and accurately access relevant information.

• Autonomous AI Agents: Design and build autonomous AI agents capable of performing complex tasks autonomously, such as customer service automation, data analysis, and decision‑making support.

• Data Preprocessing and Feature Engineering: Prepare and preprocess large datasets, perform feature extraction and engineering, and ensure data quality for training and validating models.

• Model Evaluation and Tuning: Conduct thorough evaluations of model performance using appropriate metrics and benchmarks. Perform hyperparameter tuning to optimize model configurations.

• Algorithm Development: Research and implement novel algorithms and techniques to improve model efficiency, scalability, and robustness.

• Deployment and Integration: Work on deploying LLMs and AI agents into production environments, ensuring seamless integration with existing systems and workflows.

• Continuous Improvement: Monitor model performance in production, diagnose issues, and implement improvements and updates as needed to maintain and enhance model effectiveness.

• Collaborative Research: Collaborate with other data scientists, machine learning engineers, and domain experts to identify new opportunities for applying LLMs and AI agents, and contribute to joint research projects.

• Documentation and Knowledge Sharing: Document your methodologies, experiments, and findings comprehensively, and share knowledge with the team through presentations, reports, and technical discussions.

What do you need to succeed?