Total 2 purchasing lead job vacancies in Maintenance / Repair category in Singapore, North East

As a Lead ML Engineer, you will play a leading role in operationalizing ML Models developed by Client data scientists. You will serve as the focal point for ML model refactoring, optimization, containerization, deployment, and quality monitoring. Your main responsibilities will include:

• Conduct reviews for compliance of the ML models in accordance with overall platform governance principles such as versioning, data / model lineage, code best practices and provide feedback to data scientists for potential improvements
• Develop pipelines for continuous operation, feedback and monitoring of ML models leveraging best practices from the CI/CD vertical within the MLOps domain. This can include monitoring for data drift, triggering model retraining and setting up rollbacks.
• Optimize AI development environments (development, testing, production) for usability, reliability and performance.
• Have a strong relationship with the infrastructure and application development team in order to understand the best method of integrating the ML model into enterprise applications (e.g., transforming resulting models into APIs).
• Work with data engineers to ensure data storage (data warehouses or data lakes) and data pipelines feeding these repositories and the ML feature or data stores are working as intended.
• Evaluate open-source and AI/ML platforms and tools for feasibility of usage and integration from an infrastructure perspective. This also involves staying updated about the newest developments, patches and upgrades to the ML platforms in use by the data science teams.

Technical Skills

• At least 10 years of experience as a ML Engineer, working with Python, ML libraries, and large-scale data processing framework.
• Advance proficiency in Python used both for ML and automation tasks.
• Experience in working with ML libraries such as Panda, NumPy, H2O, or TensorFlow.
• Experience in working with large-scale data processing framework, such as Hadoop, Spark, or Dask.
• Knowledge of OpenShift / Kubernetes is a must-have.
• Good knowledge of Bash and Unix/Linux command-line toolkit is a must-have.
• Hands on experience building CI/CD pipelines orchestration by Jenkins, GitLab CI, GitHub Actions or similar tools is a must-have.
• Knowledge in the operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g., Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, Dataiku, H2O, or DKube).
• Knowledge of Workflow Orchestrator, such as Airflow or Ctrl-M.
• Knowledge of Logging and Monitoring tools, such as Splunk and Geneos.
• Experience in defining the processes, standards, frameworks, prototypes and toolsets in support of AI and ML development, monitoring, testing and operationalization.
• Experience in ML operationalization and orchestration (MLOps) tools, techniques and platforms. This includes scaling delivery of models, managing and governing ML Models, and managing and scaling AI platforms.
• Knowledge of cloud platforms (e.g. AWS, GCP) would be an advantage.

• Consistently adhere to written and verbal instructions for safe and efficient operation of plant equipment, site services management, and vehicle loading/unloading, with responsibilities varying by site.
• Propose safety and efficiency improvements within the work area, collaborating in multidisciplinary teams to prioritize and implement enhancements.
• Communicate effectively with production team members and onsite functions through verbal, email, and written channels, ensuring clarity and accuracy.
• Take accountability for personal and others' safety, adhering to site-specific safety standards, including risk assessment and incident reporting.
• Recognize the importance of attention to detail and adherence to instructions, understanding the potential consequences of oversight.
• Make routine operational decisions following established procedures, seeking guidance for non-routine decisions.
• Plan and organize work and materials for current and upcoming shifts, aligning with site logistics and production plans.
• Provide training and mentorship to new team members.
• Perform maintenance tasks and agreed-upon analytical testing as required, based on site-specific needs.
• Manage emergency situations according to established procedures, potentially participating in site emergency response activities outside regular hours.

• NITEC / Diploma in Chemical Engineering, minimum 4-5years experiences in chemical, petrochemical and oil & gas industry

Interested candidates can forward their CVs in MS Word format to [email protected]

• Reg No. R1875181
• Triton AI Pte Ltd
• License no. 21C0661

• Gross Salary up to $6,500 with AWS + 2 months bonus
• Working location at Jurong Island
• Working Hours: 7.15am to 7.15pm (12 hours shift)