Rail Asset Management - GenAI and Deployment Engineer (Senior/ Manager) - #1112628

Primary Objectives of Position
• To support the development of Generative Artificial Intelligence (GenAI) for SBS Transit.

Major Responsibilities
• Review the design and implementation of GenAI and Analytics pipelines to ensure that availability, replication, redundancy, compute, memory, disk space and any other relevant requirement is achieved prior to deployment.

• Responsible for hands-on deployment of GenAI and Analytics pipelines into production environments in both cloud and/or on-premise air-gapped machines via a variety of methods including event-driven serverless calls,

orchestration of services in bare-metal servers.

• Identify and understand the dependencies between the various pipelines and processing flows in the microservices architecture.

• Work with data engineers and data scientists to ensure that (a) Extract- Transform-Load (ETL) pipelines, (b) trained Machine Learning (ML) or GenAI models and (c) dashboards are successfully deployed.

• Manage analytics projects as the project manager, including engagement of stakeholders, supervision of contractors, handling of procurement processes and ensuring that contractors comply with SBST’s guidelines.

The above activities are by no means exhaustive and are subject to amendment whenever needed.

Specific Safety Responsibilities
Ensuring contractors comply to SBST’s Safety rules and procedures

Job Specifications
Minimum Education / Qualifications
• Bachelor degree in in computer science, artificial intelligence, data analytics or a relevant field.
 

Minimum Years of Relevant Experience
• 8 years (for Senior Manager) or 5 years (for Manager) of experience deploying analytics solutions into production.

• 5 years (Senior Manager) or 3 years (for Manager) of experience dealing with large data sets.

Knowledge/Skills
• Proficient with analytics tools such as Python, SQL, Kubernetes, Docker, Jenkins, Apache Kafka/Spark/Nifi, Git, Shell, Bash or equivalent.

• Familiar with deep learning frameworks such as Tensorflow, PyTorch etc.

• Understanding of numerical computation or Machine Learning libraries like Numpy, Scikit-Learn, Pandas.

• Knowledge of ML/AI model development, designing dashboards, managing big data, project management and data governance concepts.

Attributes (functional & leadership competencies)
• Independent, intellectually curious, creative, diligent, and a holistic thinker.

• Resourceful, self-motivated and results-oriented.

• Able to mentor junior data engineers and data scientists.

• Able to effectively manage different stakeholders and comfortable with administrative overheads.