1. Research & Development
- Design, build, and implement novel algorithms and custom models from the ground up to solve complex, open-ended business problems where standard solutions are insufficient.
- Architect, fine-tune, and evaluate Large Language Models (LLMs) using advanced techniques such as prompt engineering, parameter-efficient fine-tuning (PEFT), and Retrieval-Augmented Generation (RAG) to build specialized, high-performing applications.
- Transform research findings into practical prototypes and products.
- Collaborate with interdisciplinary teams to apply AI research outcomes to production systems.
- Evaluate and refine existing models to improve performance and accuracy.
- Analyze large datasets to derive actionable insights and improve existing systems.
2.Implementation & Application
- Build, fine-tune, and deploy LLMs and other machine learning models for real-world applications across different domains.
- Implement AI solutions using advanced statistical techniques, deep learning architectures, and cutting-edge frameworks.
- Conduct experiments to validate the effectiveness of models and algorithms in practical scenarios.
- Collaborate with cross-functional teams, including product managers and engineers, to integrate solutions into products.
- Apply innovative approaches to extract meaningful insights from unstructured and structured data.
- Ensure research can be translated into practical applications for product development.
3. Knowledge Sharing & Collaboration
- Stay ahead of the latest developments in AI, machine learning, and large language models, actively evaluating emerging technologies for business value.
- Mentor junior researchers and contribute to the development of research proposals.
- Communicate complex technical findings effectively in fluent, professional English to both technical and non-technical stakeholders.
- Collaborate with academic institutions and industry partners on research projects.
What You Need To Maximize Your Contribution
1. Education & Experience
- Bachelor/Master degree or equivalent experience in Computer Science, Data Science, Machine Learning, Artificial Intelligent or a related technical field.
- 3+ years of experience in AI research with a strong track record of delivering applied solutions.
- Demonstrable experience in AI research, preferably with publications in renowned journals.
- Experience working with various AI techniques, including supervised and unsupervised learning, reinforcement learning, and neural networks.
2. Technical Skills
- Strong proficiency in programming languages such as Python, R.
- Deep understanding of machine learning algorithms, neural networks, and computational statistics.
- Experience with statistical analysis and experimental design.
- Proficiency in data manipulation and analysis using tools like SQL and Pandas.
- Expertise with machine learning libraries and frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Knowledge of software engineering principles and practices (optional).
3. Soft Skills
- Strong analytical and problem-solving skills.
- Excellent communication skills to convey complex findings to both technical and non-technical audiences.
- Ability to work independently and collaboratively within a team environment.
- Creativity and curiosity with a willingness to explore new ideas and approaches.
- Critical thinking skills to tackle complex challenges.
- Passion for continuous learning and adapting to new technological advancements.
