Nlp Engineer

**ABOUT US**

LawNet Technology Services (LTS) is the technology company behind LawNet, Singapore’s leading portal for legal research, information and transactions. An indispensable tool for the legal community since 1990, LawNet is subscribed by a majority of Singapore lawyers and is also accessible by anyone outside the profession. Users can conduct research on Singapore primary legal materials (Singapore Law Reports, unreported judgments and legislation) and secondary materials (such as Parliamentary reports, legal news, textbooks and journals). LawNet continues to enhance its services and content while maintaining its affordable and highly competitive subscription rates, making it an essential resource for the legal community.

LTS is a wholly owned subsidiary of the Singapore Academy of Law (SAL), a promotion and development agency for Singapore’s legal industry. In addition to running LawNet, LTS manages the technology driving SAL’s support services for Singapore’s legal industry and statutory functions such as stakeholding services and appointment of Senior Counsel, Commissioners for Oaths and Notaries Public.

Led by a Board of Directors who understands both the capabilities of technology and the needs of the legal profession, LTS continues to develop bold and innovative products and services that will better serve the needs of the legal community.

**POSITION**
NLP Engineer

**REPORTING STRUCTURE**

The NLP Engineer will report to the Senior AI Solutions Architect of LawNet Technology Services (LTS).

**ABOUT THE ROLE**
As an NLP Engineer at LTS, you will play a crucial role in developing and enhancing our AI-driven products and services. We're seeking a skilled Data/NLP Engineer to join our team to develop and drive a data strategy to optimise model performance and manage the data pipelines for training and evaluating our custom LLMs.

**RESPONSIBILITIES**
- Work closely with domain experts to understand data sources, formats and structure.
- Collaborate with Machine Learning Engineers and MLOps Engineers to understand data requirements for LLM training and inference.
- Engage in data engineering tasks, including data collection, preprocessing, quality control and augmentation to ensure high-quality datasets for model training.
- Develop from scratch and fine-tune both traditional NLP and LLM models on LTS cloud infrastructure, optimizing for safety and performance (measurable via benchmarks)
- Collaborate with domain experts and carry out independent research to optimise and develop product evaluation suites to measure model and product performance.
- Explore and implement techniques for mitigating bias and ensuring fairness within the training data for the LLM.
- Lead in the development and implementation of strategies for data augmentation improve LLM performance.
- Collaborate with AI and MLOps Engineers to integrate NLP models into automated pipelines.
- Stay abreast of the latest NLP techniques and methodologies, experimenting with new models and approaches to maintain our competitive edge.
- Conduct rigorous testing and validation of NLP models to ensure robustness and reliability.Document and present model development processes, results, and insights to both technical and non-technical stakeholders.

**SKILLS & QUALIFICATIONS**
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Linguistics, or a related field.
- Proven experience in NLP or related areas, with a strong portfolio of projects demonstrating expertise in data engineering and model training/finetuning.
- Proficiency in programming languages such as Python, and experience with NLP libraries (e.g., NLTK, spaCy, Hugging Face).
- Experience with cloud computing platforms and services, and the ability to deploy and manage large models in a cloud environment.
- Strong problem-solving skills, with a creative and analytical approach to tackling complex challenges.Excellent communication and collaboration skills, with the ability to work effectively in a team environment.

Please provide your resume, including details of your currently monthly salary, total annual compensation package, and salary expectations.