Total 17 job vacancies by Base Camp Digital Pte. Ltd.

IT Cybersecurity Project Manager

Overview

Looking for a hands-on Cybersecurity Project Manager to drive key security and data protection initiatives across a large enterprise environment. This role blends project delivery, technical understanding, and stakeholder management.

Whats on offer: 

• Job Type: Contract role
• Location: Singapore

What You’ll Do

• Lead cybersecurity projects (DLP, SIEM, data classification, cloud security)
• Manage end-to-end delivery: timelines, risks, stakeholders, vendors
• Work closely with tech teams to implement and optimise security solutions
• Translate security/privacy requirements into practical implementations
• Drive data protection initiatives and support regulatory compliance
• Provide clear updates to leadership on progress and risks

What We’re Looking For

• 5–8 years in cybersecurity, infrastructure, or security project delivery
• Strong experience in DLP / SIEM / cloud security / IAM projects
• Solid understanding of security concepts, networks, and cloud (AWS preferred)
• Comfortable working with cross-functional teams and vendors
• Exposure to regulatory frameworks and data protection standards

Nice to Have

• PMP or equivalent
• AWS / Cloud Security certifications
• Security or privacy certifications

Job Summary:

We are seeking a Machine Learning Platform Engineer (MLOps) to bridge the gap between data science and production systems within our ML & AI platform team. The role spans the full ML lifecycle—from development to deployment—focusing on building robust data pipelines, managing model lifecycles, and enabling scalable AI integrations. You will also drive agentic workflows to support autonomous, AI-powered solutions in collaboration with cross-functional teams.

Job Responsibilities:

• Design, develop and deploy machine learning solutions and services
• Implement end-to-end machine learning pipelines from data ingestion to training and model serving
• Operationalize LLMs, embeddings, and multi-agent systems in real-world applications
• Manage the machine learning and model lifecycle (experimentation, registry, deployment)
• Oversee the model promotion lifecycle, coordinating validation gates and approval workflows to safely deploy new model versions from stating to production
• Containerize applications using Docker and orchestrate them via Kubernetes
• Build and maintain CI/CD pipelines for ML models and LLM applications
• Collaborate with data scientists to refactor research code into production-ready Python code
• Monitor model performance, data drift, and performance in production
• Assess and integrate AI solutions ensuring optimal performance and reliability
• Design and implement production grade RAG systems
• Collaborate with infrastructure teams, data engineers, data scientists, and other stakeholders to integrate machine learning solutions into existing systems and processes
• Participate in code reviews, testing, and debugging to ensure the quality and reliability of machine learning solutions

Job Requirements:

• Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, or a related field
• Advanced proficiency in Python programming with a focus on writing clean, testable and efficient code
• DevOps & Containers: Proficient with Docker for containerization and working knowledge of Kubernetes (k8s) for orchestration
• Practical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloads
• MLOPS tools: hands on experience with MLflow (or similar tools like weights & biases) for experiment tracking and model registry
• Proven experience working with Large Language Models (LLMs)
• Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning patterns
• Experience with data preprocessing, feature engineering, and model selection and evaluation techniques
• Hands-on experience with CI/CD pipelines (GitLab, Jenkins)
• Knowledge of statistical and mathematical concepts relevant to machine learning, such as probability, linear algebra, and optimization
• Relevant work experience in machine learning, data science or a related field