NLP Engineer

Crossing Hurdles

Position:

Role Responsibilities:

• Build NLP pipelines for text classification, tokenization, entity extraction, and RAG workflows.
• Design scalable text analytics and preprocessing pipelines for multilingual datasets.
• Implement and fine-tune NLP models using Hugging Face Transformers, spaCy, tokenizers, and PyTorch.
• Develop evaluation frameworks to measure model accuracy, robustness, and performance across diverse corpora.
• Construct retrieval and neural search components that integrate NLP with IR systems.
• Work with JSON, JSONL, and large text datasets to create efficient, production-ready data flows.
• Collaborate with AI researchers and engineers to integrate NLP solutions into larger AI systems.
  

Requirements:

• Background in computer science, computational linguistics, or applied machine learning.
• Proficiency with Hugging Face Transformers, PyTorch, spaCy, and modern tokenization libraries.
• Experience working with multilingual corpora and text-based datasets (JSON/JSONL).
• Understanding of NLP tasks such as classification, NER, tokenization, embedding generation, and retrieval.
• Experience building scalable text preprocessing and data pipelines.
• Ability to fine-tune and evaluate models across varied benchmarks and languages.
• Curiosity about building RAG systems and integrating neural search with NLP workflows.
  

Key Domains:

Candidates with expertise in the following areas are encouraged to apply:

• Natural Language Processing:

  Tokenization, embeddings, NER, multilingual NLP

• Machine Learning:

  Model fine-tuning, PyTorch workflows, Transformer architectures

• Information Retrieval:

  RAG, neural search, retrieval pipelines

• Text Analytics:

  Preprocessing, evaluation frameworks, large-scale text pipelines

• AI Systems:

  Integration of NLP components into broader AI infrastructure

Application Process:

• Apply for the job role
• Await the official message/email from our recruitment team (typically within 1-2 days)