Total 8 information technology job vacancies in Engineering category in d10 ardmore bukit timah holland road tanglin

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.
• 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).
• 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.

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.

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

MLOps Engineer

REPORTING STRUCTURE

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

ABOUT THE ROLE

We are seeking an experienced MLOps Engineer specializing in large language models at LawNet Technology Services. You will be at the forefront of operationalizing and scaling our AI capabilities. This role demands a unique blend of skills in machine learning, software engineering, and cloud technologies to efficiently deploy, monitor, and manage our large language models in production. Your expertise will directly contribute to the reliability, efficiency, and scalability of our AI services.

RESPONSIBILITIES

• Design and implement robust MLOps pipelines tailored for large language models, ensuring seamless transition from development to production.
• Work closely with data scientists and AI researchers to automate and optimize the model training, fine-tuning, inference and evaluation processes for our large language models on cloud infrastructure.
• Ensure the LLM can be seamlessly integrated with the serving infrastructure managed by the software engineering team.
• Explore and implement techniques for continuous learning of the LLM as new user data becomes available.
• Develop and implement strategies for monitoring model performance (accuracy, latency, fairness) in production.
• Set up alerts and dashboards to proactively identify and troubleshoot potential issues with the LLM.
• Stay up-to-date on the latest advancements in MLOps tools and methodologies.
• Implement monitoring and logging systems to track model performance, resource utilization, and operational health, enabling proactive maintenance and optimization.
• Ensure compliance with data privacy and security protocols throughout the model lifecycle.
• Facilitate continuous integration and delivery (CI/CD) processes for machine learning models, incorporating automated testing and quality assurance practices.
• Collaborate with stakeholders to understand operational requirements and challenges, translating them into technical solutions that enhance our AI capabilities.
• Stay updated with the latest advancements in MLOps, cloud technologies, and large language model development, incorporating best practices to maintain our competitive edge.

SKILLS & QUALIFICATIONS

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
• Solid experience in MLOps, DevOps, or software engineering, with a specific focus on machine learning and large language models.
• Strong experience in building and managing machine learning pipelines.
• Proficiency in cloud computing platforms (AWS, GCP, Azure) and services related to machine learning and large-scale computing.
• Strong background in containerization and orchestration technologies (Docker, Kubernetes), as well as infrastructure as code (Terraform, CloudFormation).
• Demonstrated ability in programming with Python and experience with ML frameworks (TensorFlow, PyTorch, Hugging Face).
• Understanding of best practices for data security, privacy, and compliance in the context of AI and machine learning.
• Excellent problem-solving skills, with the ability to work in a fast-paced, evolving 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.

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.

Job Responsibilities

- Support the production of scalable and optimised AI or machine learning (ML) models.

- Focus on building algorithms for the extraction, transformation and loading of large volumes of real-time, unstructured data to deploy AI or ML solutions from theoretical data science models.

- Run experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process.

- Work in a team setting and apply knowledge in statistics, scripting and programming languages required by the firm.

- Work with the relevant software platforms in which the models are deployed.

Job Requirement

- Bachelor’s degree in Computer Science/ Information Technology/ Programming & Systems Analysis/ Science (Computer Studies)

- Minimum 10 months’ work experience in AI engineer

Job Responsibilities

- Support the production of scalable and optimised AI or machine learning (ML) models.

- Focus on building algorithms for the extraction, transformation and loading of large volumes of real-time, unstructured data to deploy AI or ML solutions from theoretical data science models.

- Run experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process.

- Work in a team setting and apply knowledge in statistics, scripting and programming languages required by the firm.

- Work with the relevant software platforms in which the models are deployed.

Job Requirement

- Bachelor’s degree in Computer Science/ Information Technology/ Programming & Systems Analysis/ Science (Computer Studies)

- Minimum 1 years’ work experience in AI engineer

Job Responsibilities:

• Support the production of scalable and optimised AI or machine learning (ML) models.
• Focus on building algorithms for the extraction, transformation and loading of large volumes of real-time, unstructured data to deploy AI or ML solutions from theoretical data science models.
• Run experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process.
• Work in a team setting and apply knowledge in statistics, scripting and programming languages required by the firm.
• Work with the relevant software platforms in which the models are deployed.

Job Qualification:

• At least a Bachelor’s degree in any of these faculties:
• Computer Science
• Information Technology
• Programming & Systems Analysis
• Science (Computer Studies)
• Minimum total one years’ work experience in at least one of these job titles: AI engineer

You will:

• Be responsible for the cloud infrastructure in terms of: scalability, availability, performance, (cost & resource) efficiency, capacity planning.
• Spend lesser than 50% of the time working through our vendors in carrying out the day-to-day IT operation such as; performance monitoring, attending to issues, manual intervention and service requests (infrastructure provisioning, deployment, data/system backups, patching and disaster recovery).
• Be responsible for system administration tasks such as OS & application patching, software upgrades, backup, restore, etc. for our cloud infrastructure (AWS, Azure).
• Drive troubleshooting, incident response/ resolution and blameless post-mortems.
• Be responsible to maintain services once they go live by measuring and monitoring availability, performance, and overall system health.
• Strive to streamline and secure cloud infrastructure management by proactively monitoring and protecting system boundaries, application deployment and release status, automating manual tasks, and keeping systems secured.
• Spend most of the time on development tasks such as writing Infrastructure-as-Code (IaC), continuous improvement, and driving initiatives to improve automation, scalability and reliability.
• Develop automation code for change control, configuration management, deployment and maintenance of infrastructure and applications through CI/CD pipeline.
• Improve service resiliency through high levels of automation, to effectively detect/ predict/ prevent issues in the environment.
• Develop and fine-tune change & incident management processes across teams.
• Scale systems sustainably through mechanisms like automation; evolve systems by pushing for changes that improve reliability and velocity.
• Automate provisioning by IaC and Configuration-as-Code (CaC) and other tasks by serverless functions (e.g. AWS Lambda, Azure Functions).
• Review resource/ workload to optimise cost.

You have:

• At least a diploma in Computing/ Computer Studies/ Information Systems/ ICT or equivalent.
• To enjoy cloud, systems and security management, and relentlessly automating work.
• Related cloud experience attested by certifications in SysOps, DevOps (AWS preferred)
• At least 3 years of hands-on experience operating and maintaining systems running on Cloud infrastructure (AWS preferred).
• Familiarity with AWS IAM.
• Proven experience in various IaC (e.g. Cloudformation and Terraform) & CaC (e.g. Ansible) tools.
• Hands-on experience administering Unix & Windows operating systems as well as automating with shell scripts.
• Deep appreciation of infrastructure and application monitoring, logging, alerting, release and configuration management.
• Deep understanding in networking (e.g. HTTP/ TLS protocol, TCP/IP, routing tables, network topology, load balancers, DNS, NTP, Network VPC/ vNET Peering).
• Experience in standard IT security practices (e.g., encryption, certificates, key management, SSH).

You will catch our attention if you:

• Have proven experience in Site Reliability Engineering (SRE), DevSecOps practices and methodologies.
• Have experience in operating containerised workloads (using Docker / Docker Compose, Kubernetes).
• Have experience operating internet-facing 24/7 high-load applications (e.g. eCommerce).
• Have experience in administering WordPress Multisite and Bitnami systems

Job Description :-

• The role will mainly manage KRIs and tech debt amongst other risk tasks
• Risk Identification: Identify and define Key Risk Indicators (KRIs) relevant to the organization's objectives and risk appetite
• Data Collection and Analysis: Collect and analyze data to determine the current status of key risk indicators, ensuring accuracy and relevance
• Monitoring and Reporting: Continuously monitor KRIs and provide regular reports to management, highlighting emerging risks and trends
• Risk Assessment: Assess the significance of deviations from established risk thresholds and escalate issues as necessary
• Communication: Effectively communicate risk insights to stakeholders, including executives and department heads
• Risk Mitigation Strategies: Collaborate with risk owners to develop and implement strategies for mitigating identified risks
• Policy Development: Contribute to the development and enhancement of risk management policies and procedures
• Regulatory Compliance: Stay informed about relevant regulations and ensure that the organization's risk management practices align with legal requirements
• Technology Utilization: Leverage technology tools for efficient KRI monitoring and reporting.

Required Skills :-

• Risk Management: Strong understanding of risk management principles and methodologies
• Analytical Skills: Proficient in data analysis and interpretation to derive meaningful insights from key risk indicators
• Communication Skills: Excellent verbal and written communication skills to convey complex risk information clearly to diverse audiences
• Collaboration: Ability to collaborate with cross-functional teams and influence decision-making related to risk management
• Problem-Solving: Strong problem-solving skills to address emerging risks and develop effective mitigation strategies
• Regulatory Knowledge: Familiarity with relevant industry regulations and compliance requirements
• Technology Proficiency: Competence in using risk management software and data analytics tools
• Attention to Detail: Meticulous attention to detail to ensure accuracy in risk data and reporting
• Adaptability: Ability to adapt to changing risk landscapes and implement proactive measures
• This role involves a combination of technical skills, strategic thinking, and effective communication to ensure the organization is equipped to manage its key risks.