Data Scientist LLM Specialist

citriot solutions pvt tld; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A leading company specializing in AI-driven solutions is seeking a Data Scientist LLM Specialist to develop and optimize large-scale language models. You will design innovative solutions for NLP tasks and deploy models across various infrastructures, collaborating with top engineers in a fast-paced environment.

Benefits

Paid sick time

Paid time off

Qualifications

• 3+ years in AI/ML, with at least 2+ years working with LLMs.
• Experience in training, fine-tuning, and deploying LLMs.

Responsibilities

• Design, fine-tune, and deploy large-scale language models.
• Implement prompt engineering and retrieve-augmented generation techniques.
• Monitor model performance using MLOps frameworks.

Skills

Python

MLOps

AI/ML

Large Language Models

Deep Learning

Education

Bachelor's or Master's in Computer Science, Data Science, AI

Tools

PyTorch

TensorFlow

Hugging Face Transformers

Job description

Position: Data Scientist LLM Specialist

Location: Bangalore (On-Site)

Experience Level: 3+ years in AI/ML with expertise in Large Language Models (LLMs)

Employment Type: Full-time

About Citriot

Citriot (https://www.citriot.ai/) is at the forefront of AI-driven solutions, specializing in developing state-of-the-art machine learning and deep learning models to solve real-world challenges. We are expanding into Large Language Models (LLMs) to drive AI innovation across industries.

Key Responsibilities

1. LLM Model Development & Optimization - Design, fine-tune, and deploy large-scale language models (GPT, LLaMA, Mistral, etc.) for real-world applications. Develop custom LLM solutions for NLP tasks such as text generation, summarization, question answering, and conversational AI. Implement prompt engineering and retrieval-augmented generation (RAG) techniques. Optimize model performance with quantization, pruning, and knowledge distillation.
2. LLM Deployment & MLOps - Deploy LLMs on cloud (AWS, Azure, GCP) and on-prem infrastructure. Utilize vector databases (FAISS, Pinecone, Chroma DB) for retrieval-augmented generation. Implement API-based LLM services and integrate with business applications. Monitor model drift and performance, ensuring continuous improvement using MLOps frameworks like MLflow, Kubeflow, and SageMaker.
3. Research & Experimentation - Stay updated with advancements in open-source LLMs (Meta AI, OpenAI, Cohere). Experiment with fine-tuning techniques like LoRA, PEFT, and adapter layers. Conduct benchmarking studies to optimize for specific use cases.
4. Performance & Scaling - Implement distributed training on multi-GPU and TPU clusters. Work with NVIDIA GPUs (A100, H100, Jetson) and optimize inference using TensorRT, ONNX, and DeepSpeed. Ensure low-latency model serving leveraging libraries like vLLM and TGI.

Qualifications

• Bachelor's or Master's in Computer Science, Data Science, AI, or related fields.
• 3+ years in AI/ML, with at least 2+ years working with LLMs.
• Proficiency in Python and frameworks like PyTorch, TensorFlow, and Hugging Face Transformers.
• Experience in training, fine-tuning, and deploying LLMs on cloud and on-prem infrastructure.
• Strong understanding of LLM architectures, transformer models, and generative AI techniques.
• Experience with vector databases (FAISS, Pinecone) and RAG.
• Hands-on experience with MLOps tools like MLflow, Kubeflow, SageMaker.

Preferred Skills

• Experience in LLM optimization techniques such as DeepSpeed, LoRA, Flash Attention, GPTQ.
• Familiarity with NVIDIA TensorRT, ONNX, vLLM, or TGI for inference optimization.
• Knowledge of distributed computing frameworks like Ray or Dask.
• Contributions to open-source LLM projects or research publications are a plus.

Why Join Us

Work on cutting-edge LLM projects shaping the future of AI. Collaborate with top AI/ML engineers and researchers. Enjoy a competitive salary, benefits, and growth opportunities.

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Job Details

• Type: Full-time
• Benefits: Paid sick time, Paid time off
• Schedule: Day shift
• Work Location: In person