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
AI/ML Engineer (Backend)
REPORTING STRUCTURE
The AI/ML Engineer will report to the Senior AI Solutions Architect of LawNet Technology Services (LTS).
ABOUT THE ROLE
As an AI/ML Engineer at LTS, you will play a crucial role in developing and enhancing our AI-driven products and services. We're seeking a talented Software Engineer to join our team and be responsible for deploying, maintaining, and scaling the infrastructure that serves our custom LLMs for a variety of use cases.
RESPONSIBILITIES
- Design, develop, and implement highly scalable backend services on AWS to support efficient training and inference of our LLM.
- Keep abreast of developments in AWS or other services to manage the LLM training and inference operations.
- Develop and optimise APIs to facilitate communication between the LLM and frontend applications for user interaction with functionalities from a variety of use cases.
- Design and implement data pipelines for efficiently moving training data to and from the LLM training environment on AWS (e.g., S3 Buckets, EFS storage).
- Integrate containerization technologies like Docker and container orchestration platforms like Amazon Elastic Kubernetes Service (EKS) for managing the LLM inference environment.
- Configure and implement robust security practices within your backend services to protect the LLM and user data at rest and in transit.
- Develop and implement logging and monitoring solutions (e.g., CloudWatch Logs, CloudWatch Metrics) to track the health and performance of the backend infrastructure and the LLM.
- Collaborate with ML and Front Eng Engineers to understand their specific requirements for training and inference, and translate those needs into efficient backend solutions.
- Collaborate with MLOps Engineers to automate the deployment and management of backend services and the LLM.
- Stay up-to-date on the latest advancements in AWS services relevant to backend development and LLM infrastructure.
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.
- Solid understanding of machine learning algorithms, language modeling, and their applications.
- Proficiency in programming languages such as Python, and experience with NLP libraries (e.g., NLTK, spaCy, Hugging Face).
- Familiarity with popular vector database platforms like Pinecone, Faiss, or Milvus would be beneficial.
- Experience with cloud computing platforms and services, and the ability to deploy and manage large models in a cloud environment.
- Experience with data pipelines and data processing tools on AWS (e.g. AWS Lambda).
- Experience with Infrastructure as Code (IaC) tools like Terraform or CloudFormation
- 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.
The level of offer and appointment designation will commensurate with applicants relevant experience and track records. Successful candidate will be offered a 2-year contract in the first instance.
Please provide your resume, including details of your currently monthly salary, total annual compensation package, and salary expectations.
