Machine Learning Engineer

Position: Machine Learning Integration Engineer

Location: Singapore

Employment Type: Full-Time

Key Responsibilities:

• Design and build scalable machine learning pipelines tailored for Generative AI and Agentic AI use cases.
• Integrate AI models into production environments using containerization platforms such as OpenShift, Docker, and Kubernetes.
• Collaborate with cross-functional teams to understand AI workflows and translate them into stable, production-ready engineering solutions.
• Create and maintain automation scripts using Python, Linux shell scripting, and other relevant tools.
• Manage seamless deployment and integration of AI services in cloud platforms, specifically AWS, Azure, and GCP.
• Implement and uphold network security protocols to protect AI models, systems, and data pipelines.
• Continuously monitor and improve system performance, scalability, and reliability.
• Contribute to and support CI/CD pipelines for AI model deployment and lifecycle management.

Required Qualifications:

• Bachelor's degree in Computer Science, Engineering, or a related field.
• Minimum of 4 years’ experience in Machine Learning engineering or AI system integration.
• Proven hands-on experience with OpenShift, Docker, and Kubernetes.
• Proficiency in cloud platforms, particularly AWS and GCP.
• Working knowledge of data and network security practices for AI systems.
• Solid understanding of Generative AI and Agentic AI principles.
• Experience in LLM prompt engineering or building RAG pipelines.
• Familiarity with API integration and microservices architecture.
• Strong proficiency in Python, both for machine learning and automation tasks.
• Advanced knowledge of Bash and Unix/Linux command-line tools.
• Experience with workflow orchestrators such as Ctrl-M.
• Familiarity with logging and monitoring tools, including Splunk and Geneos.
• Understanding of observability frameworks such as Langfuse, Elastic Stack, Grafana, and OpenTelemetry.