Data Scientist (LLM)

Overview

On Site
$70 - $80
Contract - W2
Contract - 12 Month(s)

Skills

Algorithms
Artificial Intelligence
Computer Networking
Ensemble
Fusion
GPU
InfiniBand
Large Language Models (LLMs)
Layout
Machine Learning (ML)
Open Source
Optical Character Recognition
Optimization
PPO
Proprietary Software
Remote Direct Memory Access
Semantic Search
Training
Vector Databases

Job Details

Job Description

We are seeking a highly skilled Data Scientist specializing in Large Language Models (LLMs) to lead end-to-end model training, fine-tuning, and optimization across both open-source (Qwen, LLaMA, Mistral) and closed-source (OpenAI, Gemini, Anthropic) ecosystems. The ideal candidate will design advanced retrieval systems, implement distributed training environments, and apply reinforcement learning methods to align AI behavior with human and domain-specific needs.

Key Responsibilities

  • Lead end-to-end training and fine-tuning of Large Language Models (LLMs).

  • Architect and implement Graph RAG pipelines for contextual knowledge grounding.

  • Build and scale distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training.

  • Apply reinforcement learning techniques (RLHF, RLAIF) to align models with business and user needs.

Required Qualifications
  • 10+ years of applied AI/ML experience with a track record of delivering production-grade models.

  • Deep expertise in:

    • LLM training & fine-tuning (GPT, LLaMA, Mistral, Qwen)

    • Graph-based retrieval systems (GraphRAG, knowledge graphs)

    • Embedding models (BGE, E5, SimCSE)

    • Semantic search & vector databases (FAISS, Weaviate, Milvus)

    • Document segmentation & preprocessing (OCR, layout parsing)

    • Distributed training frameworks (NCCL, Horovod, DeepSpeed)

    • High-performance networking (InfiniBand, RDMA)

    • Model fusion & ensemble methods (stacking, boosting, gating)

    • Optimization algorithms (Bayesian, Particle Swarm, Genetic)

    • Symbolic AI & rule-based systems

    • Meta-learning & Mixture of Experts architectures

    • Reinforcement learning (RLHF, PPO, DPO)

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