Total 5 manufacturing engineer job vacancies in apac singapore singapore fusionopolis

We are seeking a skilled and motivated Data Engineer with expertise in Hadoop, Spark, OpenShift Container Platform (OCP), and DevOps practices. As a Data Engineer, you will be responsible for designing, developing, and maintaining efficient data pipelines, processing large-scale datasets. Your expertise in Hadoop, Spark, OCP, and DevOps will be crucial in ensuring the availability, scalability, and reliability of our ML Solutions.

Responsibilities:

• Implement data transformation, aggregation, and enrichment processes to support various data analytics and machine learning initiatives
• Collaborate with cross-functional teams to understand data requirements and translate them into effective data engineering solutions
• Ensure data quality and integrity throughout the data processing lifecycle
• Design and deploy data engineering solutions on OpenShift Container Platform (OCP) using containerization and orchestration techniques
• Optimize data engineering workflows for containerized deployment and efficient resource utilization
• Collaborate with DevOps teams to streamline deployment processes, implement CI/CD pipelines, and ensure platform stability
• Implement data governance practices, data lineage, and metadata management to ensure data accuracy, traceability, and compliance
• Monitor and optimize data pipeline performance, troubleshoot issues, and implement necessary enhancements
• Implement monitoring and logging mechanisms to ensure the health, availability, and performance of the data infrastructure
• Document data engineering processes, workflows, and infrastructure configurations for knowledge sharing and reference
• Stay updated with emerging technologies, industry trends, and best practices in data engineering and DevOps
• Provide technical leadership, mentorship, and guidance to junior team members to foster a culture of continuous learning and innovation to the continuous improvement of the analytics capabilities within the bank

• Bachelor's degree in Computer Science, Information Technology, or a related field
• Proven 2-5 Years of experience as a Data Engineer, working with Hadoop, Spark, and data processing technologies in large-scale environments
• trong expertise in designing and developing data infrastructure using Hadoop, Spark, and related tools (HDFS, Hive, Pig, etc)
• Experience with containerization platforms such as OpenShift Container Platform (OCP) and container orchestration using Kubernetes
• Proficiency in programming languages commonly used in data engineering, such as Spark, Python, Scala, or Java
• Knowledge of DevOps practices, CI/CD pipelines, and infrastructure automation tools (e.g., Docker, Jenkins, Ansible, BitBucket)
• Experience with Graphana, Prometheus, Splunk will be an added benefit
• trong problem-solving and troubleshooting skills with a proactive approach to resolving technical challenges
• Excellent collaboration and communication skills to work effectively with cross-functional teams
• Ability to manage multiple priorities, meet deadlines, and deliver high-quality results in a fast-paced environment
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and their data services is a plus

We seek individuals with highly developed conceptual, strategic, and analytical skills, capable of striking a balance between visionary thinking and practical solutions. The ability to comprehend, inspire, and mobilize others is crucial. A business-oriented mindset coupled with effective storytelling will drive your success. We are looking for self-starters ready to take on responsibilities with enthusiasm.

Roles & Responsibilities

• Developing and maintaining the entire DevOps pipelines.
• Coordinate with the stakeholders from requirements gathering until successful implementation DevOps pipeline on boarding.
• Work with the vendors and for internal platforms to setup centralized DevOps solutions considering market best practice, Industry standard and ease of support.
• Work with the vendors and for internal platforms to upgrade the DevOps tools farm and integrating with tools.
• Lead analysis and resolution of root cause for Test Orchestration / Environment related issues
• Ability to multitask and work in a fast-paced, collaborative team environment.
• Excellent written and oral communication skills; writing, publishing and conference-level presentation skills
• Stays current with industry trends and leads development of key DevOps, Runtime, and Operational innovation platforms.

• Deep technical background, with hands-on experience in supporting CI/CD pipelines (Bitbucket, Jenkins, Artifactory, Sonar, Veracode for SAST, JIRA and Confluence).
• Hands on experience building CI/CD pipelines orchestration by Jenkins, GitLab CI, GitHub Actions or similar tools is a must-have.
• Skill to containerize a application and good if Kubernetes
• Good Experience on OpenShift or AWS
• Prior technical background on different build and branching strategies.
• Deep technical background on Kubernetes deployment process.
• Good knowledge on maintaining/deploying secrets in Kubernetes.
• Prior working experience in setting up Jira workflow.
• Strong hands-on experience on Shell or Groovy scripting.
• Prior experience is customizing JIRA and Setting up confluence spaces.
• Prior hand-on experience in setting up CI/CD pipeline as a centralized solution.
• Hand on experience in setting up DevOps governance, Security standards and audit requirements.
• Prior experience in setting up DevOps pipeline in internal and external cloud offerings.
• Experience with continuous testing, techniques and tools (Junit, Sonar Cube, Veracode etc.)
• Technical exposure of automation accomplished with advanced Python, Chef, Ruby and PowerShell
• Working knowledge of Murex, MxTest, Mx pipeline and Machine Learning applications will be an advantage.

Join our global team to help design, implement, improve, and operate our worldwide Internet Breakouts as well as the VPN Services for Bosch Users and partners. Ensure that the security infrastructure deployment and configurations align with industry standards, compliance requirements, and organizational security policies. Provide Level-2/Level-3 Support for our service portfolio, including shared on-call duty (remote). Collaborate closely with our Architects, Engineers, internal customers, partners, and stakeholders. Initiate, manage, and implement changes to our security infrastructure.

Responsibilities:

• Design, implement, and operate worldwide Internet Breakouts and VPN Services for Bosch Users and partners.
• Ensure security infrastructure deployments and configurations comply with industry standards, organizational policies, and compliance requirements.
• Provide Level-2 and Level-3 support for the service portfolio, including participation in shared on-call duty (remote).
• Work closely with Architects, Engineers, internal customers, partners, and stakeholders.
• Initiate, manage, and implement changes to the security infrastructure.

We seek individuals with highly developed conceptual, strategic, and analytical skills, capable of striking a balance between visionary thinking and practical solutions. The ability to comprehend, inspire, and mobilize others is crucial. A business-oriented mindset coupled with effective storytelling will drive your success. We are looking for self-starters ready to take on responsibilities with enthusiasm.

As an ML Engineer, your pivotal role involves operationalizing ML Models developed by Bank 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.

• Proficiency in Python used both for ML and automation tasks
• 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 of OpenShift / Kubernetes is a must-have.
• Good understanding of ML libraries such as Panda, NumPy, H2O, or TensorFlow.
• 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 Distributed Data Processing framework, such as Spark, or Dask.
• 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.

We are seeking a talented and experienced Senior Data Engineer (Quantexa)with expertise in Hadoop, Scala, Spark, Elastic, Open Shift Container Platform (OCP) and DevOps practices. Elasticsearch to join our team. As a Data Engineer, you will play a crucial role in designing, developing, and optimizing big data solutions using Apache Spark, Scala, and Elasticsearch. You will collaborate with cross-functional teams to build scalable and efficient data processing pipelines and search applications. Knowledge and experience in the Compliance / AML domain will be a plus. Working experience with Quantexa tool is a must.

Responsibilities:

·        Implement data transformation, aggregation, and enrichment processes to support various data analytics and machine learning initiatives

·        Collaborate with cross-functional teams to understand data requirements and translate them into effective data engineering solutions

·        Design, develop, and implement Spark Scala applications and data processing pipelines to process large volumes of structured and unstructured data

·        Integrate Elasticsearch with Spark to enable efficient indexing, querying, and retrieval of data

·        Optimize and tune Spark jobs for performance and scalability, ensuring efficient data processing and indexing in Elasticsearch

·        Implement data transformations, aggregations, and computations using Spark RDDs, DataFrames, and Datasets, and integrate them with Elasticsearch

·        Develop and maintain scalable and fault-tolerant Spark applications, adhering to industry best practices and coding standards

·        Troubleshoot and resolve issues related to data processing, performance, and data quality in the Spark-Elasticsearch integration

·        Monitor and analyze job performance metrics, identify bottlenecks, and propose optimizations in both Spark and Elasticsearch components

·        Ensure data quality and integrity throughout the data processing lifecycle

·        Design and deploy data engineering solutions on OpenShift Container Platform (OCP) using containerization and orchestration techniques

·        Optimize data engineering workflows for containerized deployment and efficient resource utilization

·        Collaborate with DevOps teams to streamline deployment processes, implement CI/CD pipelines, and ensure platform stability

·        Implement data governance practices, data lineage, and metadata management to ensure data accuracy, traceability, and compliance

·        Monitor and optimize data pipeline performance, troubleshoot issues, and implement necessary enhancements

·        Implement monitoring and logging mechanisms to ensure the health, availability, and performance of the data infrastructure

·        Document data engineering processes, workflows, and infrastructure configurations for knowledge sharing and reference

1. More than 5 years of experience as a Data Engineer
2. · Bachelor's or Master's degree in Computer Science, Software Engineering, or a related discipline
3. · Possession of Quantexa certification as a Data Engineer or Data Architect, with proficiency in the tool
4. · Demonstrated experience as a Data Engineer, utilizing Hadoop, Spark, and data processing technologies in large-scale environments
5. · Expertise in the Scala programming language and familiarity with functional programming principles
6. · Prior experience with the Quantexa tool is highly desirable
7. · Comprehensive understanding of Apache Spark architecture, including RDDs, DataFrames, and Spark SQL
8. · Advanced proficiency in designing and developing data infrastructure utilizing Hadoop, Spark, and associated tools (HDFS, Hive, Pig, etc.)
9. · Experience with containerization platforms such as OpenShift Container Platform (OCP) and container orchestration via Kubernetes
10. · Proficiency in programming languages commonly employed in data engineering, including Spark, Python, Scala, or Java
11. · Knowledge of DevOps methodologies, CI/CD pipelines, and infrastructure automation tools (e.g., Docker, Jenkins, Ansible, BitBucket)
12. · Experience with Graphana, Prometheus, and Splunk will be considered an added advantage
13. · Background in integrating and utilizing Elasticsearch for data indexing and search applications
14. · Solid understanding of Elasticsearch data modeling, indexing strategies, and query optimization techniques
15. · Experience with distributed computing, parallel processing, and handling large datasets
16. · Proficient in performance tuning and optimization methods for Spark applications and Elasticsearch queries
17. · Strong problem-solving and analytical capabilities with the capacity to debug and resolve intricate issues
18. · Familiarity with version control systems (e.g., Git) and collaborative development environments