Senior Data Scientist Jobs

Beyond Limits

Location:
Taiwan

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Not provided

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Job Description:

We are seeking a Senior Data Scientist with deep expertise in unstructured data (audio, speech, text, images, etc.) and a strong background in deploying Large Language Models (LLMs) and AI models at scale. This role focuses on real-world implementation, ensuring that models are efficient, scalable, and optimized for enterprise deployment. You will work closely with large enterprises, delivering AI-powered solutions that meet real-world performance benchmarks (speed, latency, throughput). The ideal candidate has hands-on experience optimizing LLMs through quantization and pruning, designing distributed training pipelines, and working with AI agents to build end-to-end products beyond just leveraging open-source tools. This role requires a deep understanding of Large Language Models (LLMs), multimodal architectures, and cutting-edge optimization techniques such as quantization, pruning, model distillation, and retrieval-augmented generation (RAG).

Job Responsibility:

Develop and deploy AI models for unstructured data (text, speech, audio, images) with a focus on enterprise-scale performance
Fine-tune, optimize, and deploy LLMs and multimodal models, integrating distributed training, quantization, and pruning techniques for efficiency
Design and implement production-ready AI solutions, ensuring scalability, low-latency inference, and high throughput
Work with AI agents and automation frameworks to create intelligent, real-world AI applications for enterprise clients
Build and maintain end-to-end LLM Ops pipelines, ensuring efficient training, deployment, monitoring, and model updates
Implement vector search and retrieval-augmented generation (RAG) systems for large-scale data solutions
Monitor AI performance using key metrics such as speed, latency, and throughput, continuously refining models for real-world efficiency
Work with cloud-based AI infrastructure (AWS, GCP) and containerized environments (Docker, Kubernetes) to scale AI solutions
Collaborate with engineering, DevOps, and product teams to align AI solutions with business needs and client requirements
Implement data curation pipelines, including data collection, cleaning, deduplication, decontamination, etc. for training high-quality AI models
Implement self-instruct and synthetic data generation techniques to enrich datasets for low-resource languages and specialized domains.

Requirements:

5+ years of hands-on experience in AI, Machine Learning, and Data Science, with a strong focus on production-scale AI
Expertise in LLMs, including fine-tuning, distributed training, quantization, and pruning techniques
Experience working with OCR, ASR, and TTS applications in real-world deployments
Proven experience deploying AI models in production, with real-world examples of scaled AI applications
Strong understanding of cloud computing, containerization (Docker, Kubernetes), and ML Ops best practices
Proficiency in Python, PyTorch, and ML libraries
Hands-on experience with vector databases and retrieval-augmented generation (RAG) architectures
Strong awareness of AI system performance benchmarks (latency, speed, throughput) and ability to optimize models accordingly
Experience working with AI agents, designing real-world intelligent automation solutions beyond just open-source experimentation
Proficiency in transformer-based architectures (BERT, GPT, LLaMA, Whisper, etc.), including pre-training, fine-tuning, and task-specific adaptation
Expertise in distributed training methodologies, including ZeRO-Offloading, Deep Speed, and FSDP
Experience in large-scale data curation including data cleaning, formatting, deduplication, decontamination, etc.

Nice to have:

Experience in multi-modal AI models that integrate text, speech, and vision
Hands-on work with self-supervised learning, few-shot learning, and reinforcement learning
Designed and deployed AI solutions for large enterprises, ensuring high availability, robustness, and business impact
Knowledge of AI inference optimization techniques for real-time applications
Design scalable AI systems using LLM inference frameworks like vLLM, Triton, or TensorRT-LLM, targeting 10–100 concurrent users
Integrate and optimize embedding models (e.g., BGE-M3, E5) and vector databases (e.g., Milvus, Pinecone, OpenSearch, FAISS)
Build Agentic AI systems using frameworks like CrewAI, LangGraph, or AutoGen, enabling autonomous task orchestration
Support language-specific adaptation and fine-tuning for Traditional Chinese, Simplified Chinese, Japanese, or Korean markets
Optimize inference and performance for diverse hardware, including NVIDIA GPUs (A100, L40, V100, A6000) or Huawei Ascend/Kunpeng NPUs.

Additional Information:

Job Posted:
December 06, 2025

Work Type:

Hybrid work

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Senior Data Scientist