NLP Engineer

Role Overview

Key Responsibilities

• Lead scalable NLP and LLM-powered solution design and development.
• Architect and optimize RAG pipelines, embeddings, vector search, and LLM applications.
• Fine-tune, evaluate, and optimize transformer-based models and LLMs for production use.
• Build data, training, inference, and model-evaluation pipelines.
• Improve model quality, latency, scalability, reliability, and cost.
• Establish practices for experimentation, evaluation, deployment, and monitoring of AI systems.
• Translate business requirements into AI and machine learning solutions.
• Collaborate with engineering, product, and stakeholder teams.
• Mentor engineers and provide technical leadership across NLP and Generative AI initiatives.
• Track emerging NLP and Generative AI research, models, frameworks, and practices.

Requirements

• At least 5 years of professional experience in NLP, machine learning, or Generative AI.
• Strong Python programming and hands-on experience with PyTorch, TensorFlow, Hugging Face, or equivalent frameworks.
• Deep understanding of NLP, transformers, LLMs, embeddings, RAG, fine-tuning, and model evaluation.
• Experience taking AI/ML solutions from experimentation to production.
• Strong software engineering and system-design skills, including APIs, data pipelines, and scalable architectures.
• Strong analytical and problem-solving skills with the ability to independently drive technical initiatives.

Preferred Qualifications

• Experience with PEFT/LoRA, quantization, inference optimization, or distributed model training.
• Experience with vector databases, MLOps, cloud platforms, or AI evaluation and observability frameworks.
• Experience building AI agents, multimodal systems, or advanced Generative AI applications.
• Contributions to open-source projects, research, publications, or applied AI/ML innovation.