Reinforcement Learning:
- Strong understanding of the fundamentals of reinforcement learning, including concepts like Markov Decision Processes, Q-learning, and policy gradients.
- Experience with modern RL libraries like OpenAI Gym, Ray's RLlib, or Stable Baselines.
- Practical knowledge of reinforcement learning in non-stationary environments and using human feedback as rewards.
Computational Skills:
- Proficiency in Python and other programming languages common in the AI/ML domain.
- Knowledge of GPU and TPU computation, especially in the context of deep learning.
Deep Understanding of LLMs:
- Familiarity with the nuances of large language models, their strengths, and potential pitfalls.
- Awareness of typical business use cases for LLMs, from customer support automation to content generation.
Low-code/No-code Platform Expertise:
- Proficiency in using and configuring platforms like Langflow.
- Experience in setting up, deploying, and managing applications on such platforms.
Agent-based Modelling Skills:
- Strong understanding of defining, designing, and implementing agents, their behaviours, and interactions.
- Ability to set up policies, constraints, and orchestrate chains of agents to fulfil specific business processes or workflows.
Research Acumen:
- Ability to read, understand, and implement findings from scientific papers, especially those related to LLMs and their optimizations.
- Contributions to the AI research community through publications, conferences, or open-source projects can be an advantage.
Educational Background:
- A Bachelor's or Masters (preferably) in Computer Science, Data Science, Artificial Intelligence, or related fields.
