Senior Data Scientist
Description
Job Responsibilities :
This role will focus on designing and developing GenAI-driven solutions for interactive systems and developer-facing tools. The ideal candidate will have strong expertise in GenAI technologies, including large language models, retrieval-augmented generation (RAG), and agentic workflows, with experience building scalable and production-ready AI services. This role involves working closely with data engineers, software engineers, and game developers to deliver robust AI tools, manage production pipelines, and enable fast iteration in real-world environments.
Design, develop, and maintain GenAI solutions for developer tools and interactive applications.
Build and extend RAG-based systems, including retrieval pipelines, indexing strategies, and prompt orchestration.
Develop agentic workflows and GenAI packages to support reasoning, planning, and tool-based execution patterns.
Collaborate with software engineering teams to design and manage production pipelines for GenAI microservices.
Work with data engineering teams to define data schemas, ingestion pipelines, and validation strategies for structured and unstructured data used by GenAI systems.
Fine-tune and adapt large language models for domain-specific tasks and performance requirements.
Partner with AI engineers to evaluate GenAI system performance using task-specific metrics, benchmarks, and qualitative analysis.
Optimize GenAI services and pipelines for latency, scalability, reliability, and cost efficiency in production environments.
Assist in the deployment, monitoring, and maintenance of GenAI services in cloud-based infrastructures.
Document system designs, workflows, experiments, and implementation details for internal knowledge sharing.
Stay updated on emerging trends and advancements in GenAI, LLMs, and agent-based systems through research and experimentation.
Consider ethical, legal, and regulatory implications in the development and deployment of AI systems.
Pre-Requisites :
Qualifications
Proven experience in developing and deploying applied machine learning or Generative AI systems in real-world applications.
Proficiency in Python; experience with additional languages is a plus.
Strong understanding of LLMs, prompt engineering, retrieval systems, and agent-based architectures.
Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
Experience building and evaluating RAG pipelines, GenAI tools, or AI-powered microservices.
Familiarity with cloud-based AI platforms and production ML workflows for training, evaluation, and deployment.
Ability to evaluate and optimize model performance using task-specific metrics and benchmarks.
Strong analytical and problem-solving skills.
Excellent written and verbal communication skills across technical and non-technical teams.
Preferred
Experience building RAG microservices, agent frameworks, or modular GenAI tooling.
Exposure to multi-GPU training, distributed experiments, or large-scale model fine-tuning.
Experience with AWS SageMaker or similar managed ML platforms.
Awareness of emerging GenAI techniques, including tool-using agents, function calling, and planning-based workflows.
Education & Experience
Master’s or PhD in a relevant field (Computer Science, AI, Machine Learning, etc.).
2+ years of applied experience in machine learning or Generative AI (academic or industry).
Travel Requirements
Role based in Singapore office, with occasional travel (up to 1 trip per year) for conferences, research collaborations, or business meetings.