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
LinkedIn Job Application Link: https://www.linkedin.com/jo ...
Website: https://www.basecamp.com.sg
