Senior LLM Engineer (NLP Specialist) - Remote

Role: Senior LLM Engineer (NLP Specialist)

Location: Remote

Duration: 6 Months

Key Responsibilities

1. LLM Development & Optimization

• Fine-tune and deploy large language models (GPT, BERT, T5, etc.) and NLP-related tasks with vision language models (Florence-2, PaliGemma) for domain-specific tasks such as text classification, summarization, and entity extraction.
• Advanced NLP techniques, ensuring accurate text recognition of plan annotations (e.g., pipe materials, dimensions).

1. Annotation Workflow Integration

• Design custom techniques effective annotations to thereby achieve desired project goal.
• Automate or streamline annotation tasks wherever possible (e.g., partial auto-labeling) to reduce manual effort and error rates.

1. Experimentation & Evaluation

• Establish robust evaluation metrics (Perplexity,Precision, recall, F1-score, Levheinstein distance, character array, Bleu score ) for NLP components, including text extraction quality.
• Set up an iterative experimentation framework to track model versioning, data changes, and performance gains over time.

1. Data Management & Accountability

• Coordinate with the Project Manager and AI Engineer to ensure new data (annotated or collected) is properly versioned and accessible.
• Implement best practices for dataset growth, including tracking who annotated or curated the data and how these changes affect model performance.

1. Deployment & Scalability

• Integrate with existing infrastructure and pipelines, ensuring minimal disruption to ongoing model development.
• Optimize model sizes and queries to reduce latency between input and output (quantization, batch optimization, etc.)

Required Qualifications

• Education & Experience

• Master s/Phd in Computer Science/Engineering, Computational Linguistics.
• Intensive hands on experience on custom LLM.

• Technical Expertise

• Proficiency in Python and deep learning frameworks (PyTorch preferred).
• Proven track record of deploying NLP or LLM systems in production (Cloud or on-prem).
• Solid understanding of tokenization, embedding techniques, and advanced fine-tuning strategies.
• Experience in leveraging open, closed, or custom annotation tools (for example- labelStudio) to coordinate between multiple annotation formats and annotation teams