As an AI Pipeline Engineer, you will bridge the gap between development and production, ensuring seamless integration of data ingestion, model training, testing and deployment processes. You will collaborate with architects, data engineers, application engineers and algorithm engineers to deliver DevOps, MLOps, LLMOps and GenAIOps solutions for various AI projects, focusing on generative AI. Roles and Responsibilities: 61 Feasibility Studies: ○ Evaluate DevOps processes and tools for AI and generative AI projects. ○ Experiment with various infrastructures, platforms, frameworks, and techniques to identify the most effective pipeline solutions. 61 Pipeline development: ○ Design end-to-end DevOps pipelines, considering scalability, performance, and security. ○ Collaborate with stakeholders to define project goals and success criteria. ○ Integrate DevOps, MLOps, LLMOps and GenAIOps to existing systems. ○ Ensure security best practices are followed throughout the processes. ○ Ensure enterprise SDLC is followed. ○ Process bug reports and release fixes. 61 Quality assurance ○ Design evaluation metrics and implement test cases to DevOps processes. ○ Integrate with various quality assurance tools such as static and dynamic application security testers, code scanners, vulnerability scanners, etc. 61 Documentation and Guidance: ○ Document DevOps processes, configurations, and troubleshooting guides. ○ Guide development teams in implementing AI DevOps, ensuring best practices and adherence to standards. Minimum Job Requirements: Must Qualifications 61 Bachelor’s or master’s degree in computer science, Engineering, or related fields. 61 Minimum of 4 years of hands-on experience in DevOps, cloud engineering, or related roles. 61 Proven experience in implementing CI, CD and CT pipelines. 61 Proficiency in containerization (Docker, Kubernetes). 61 Experience in DevOps tools, such as GitHub, Bitbucket, Maven, Nexus, Jenkins, Ansible, JFrog, Liquibase. 61 Knowledge in cybersecurity. 61 Good team player. 61 Strong communication skills in English, Mandarin, or Cantonese. Preferred Qualifications 61 Experience in deploying and monitoring generative AI applications. 61 Hands-on experience in software quality assurance tools, such as SonarQube, Veracode, Prisma, Qualys, etc. 61 Experience in automated testing tools, such as JMeter, Selenium, PyTest, Junit, Mocha, RPA, etc. 61 Experience in monitoring tools, such as Dynatrace, Spunk, Kibana. 61 Relevant certifications in DevOps, cloud, and machine learning. 61 Experience in Agile/Scrum software development process. 61 Ability to work effectively in cross-functional teams. 61 Enthusiasm for automating deployment, monitoring, and maintenance processes.
