NLP Data Scientist

NLP Data Scientist / LLM Fine-Tuning Specialist

• Employment Type: Contract
• Work Mode: Remote
• Location: Offshore
• Total Experience
  
  Required:
  
  4 to 8 years
• Relevant Experience
  
  Required:
  
  3+ years of dedicated natural language processing (NLP) and hands-on Large Language Model (LLM) fine-tuning experience
• Mandatory Certification: Google Cloud Certified Professional Machine Learning Engineer or AWS Certified Machine Learning - Specialty

We are seeking an experienced NLP Data Scientist / LLM Fine-Tuning Specialist to take ownership of our specialized open-source model optimization tracks. The ideal candidate will possess deep expertise in deep learning, dataset preparation, and parameter-efficient training methodologies to fine-tune foundational models for industry-specific terminology, domain-specific reasoning, and custom task execution.

• Lead LLM fine-tuning initiatives
  , leveraging Parameter-Efficient Fine-Tuning techniques (PEFT) including LoRA, QLoRA, Prefix Tuning, and Prompt Tuning to optimize open-source architectures (e.g., Llama, Mistral).
• Curate, clean, and structure high-quality training datasets
  , implementing automated data deduplication, tokenization schemes, synthetic data generation pipelines, and human-in-the-loop validation frameworks.
• Implement advanced reinforcement learning alignment layers
  , configuring Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO) to enforce model safety, helpfulness, and tone guardrails.
• Optimize model footprint constraints and memory overhead
  , applying post-training quantization techniques (e.g., GGUF, AWQ, GPTQ) to minimize parameter degradation and compute budgets.
• Design rigorous evaluation benchmarks and metrics panels
  , executing automated validation tests (e.g., BLEU, ROUGE, custom verification matrices) to audit model hallucinations, factual accuracy, and domain alignment.
• Manage distributed deep learning training jobs
  , scaling pipeline configurations, tensor parallelism parameters, and gradient checkpointing scripts across multi-GPU compute blocks.
• Collaborate with MLOps infrastructure teams
  , formatting completed model weight checkpoints cleanly for scalable cloud deployment and real-time inference serving layers.

• 4 to 8 years of core data science or advanced machine learning engineering experience, with 3+ dedicated years actively training, evaluation, and fine-tuning natural language processing systems.
• Expert-level technical mastery of Python, deep learning frameworks (PyTorch), transformer architectures (Hugging Face Transformers, Accelerate, PEFT), and vector calculations.
• Deep structural understanding of attention mechanisms, tokenization constraints, context window degradation behaviors, loss function optimization, and hardware compute limitations (CUDA).
• Mandatory certification: Professional ML Engineer or Specialty Machine Learning credential from a major cloud vendor (AWS/GCP).

• Master’s or Ph.D. in Computer Science, Data Science, Computational Linguistics, or an adjacent quantitative field with a research focus on neural network text models.
• Prior experience implementing custom embedding model structures or optimizing domain-specific classification layers inside constrained enterprise runtimes.