Data Scientist

Yochana IT Solutions

Minneapolis, MN, United States

6 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

8 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Artificial Intelligence Automated Storage and Retrieval Systems Data Transformation Distributed Computing Environment Genetic Algorithm Graph Database InfiniBand Machine Learning Open Source Technology Remote Direct Memory Access Search Technologies Software Deployment

+6 more

Reinforcement Learning Large Language Models Database Optimization Information Technology Optimization Algorithms GPT

Job description

Lead end-to-end training and fine-tuning of Large Language Models (LLMs), including both open-source (e.g., Qwen, LLaMA, Mistral) and closed-source (e.g., OpenAI, Gemini, Anthropic) ecosystems.
Architect and implement GraphRAG pipelines, including knowledge graph representation and retrieval for enhanced contextual grounding.
Design, train, and optimize semantic and dense vector embeddings for document understanding, search, and retrieval.
Develop semantic retrieval systems with advanced document segmentation and indexing strategies.
Build and scale distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training.
Apply reinforcement learning techniques (e.g., RLHF, RLAIF) to align model behavior with human preferences and domain-specific goals.
Collaborate with cross-functional teams to translate business needs into AI-driven solutions and deploy them in production environments.

Requirements

PhD or Master’s degree in Computer Science, Machine Learning, or related field.
8+ years of experience in applied AI/ML, with a strong track record of delivering production-grade models.

Deep expertise in:

LLM training and fine-tuning (e.g., GPT, LLaMA, Mistral, Qwen)
Graph-based retrieval systems (GraphRAG, knowledge graphs)
Embedding models (e.g., BGE, E5, SimCSE)
Semantic search and vector databases (e.g., FAISS, Weaviate, Milvus)
Document segmentation and preprocessing (OCR, layout parsing)
Distributed training frameworks (NCCL, Horovod, DeepSpeed)
High-performance networking (InfiniBand, RDMA)
Model fusion and ensemble techniques (stacking, boosting, gating)
Optimization algorithms (Bayesian, Particle Swarm, Genetic Algorithms)
Symbolic AI and rule-based systems
Meta-learning and Mixture of Experts architectures
Reinforcement learning (e.g., RLHF, PPO, DPO)

Bonus Skills

Experience with healthcare data and medical coding systems (e.g., CPT, CM, PCS).
Familiarity with regulatory and compliance frameworks in AI deployment.
Contributions to open-source AI projects or published research. And/Or ability to take research papers to poc production.