NLP Engineer

Role Overview

We are looking for an NLP Engineer to lead the design, development, and deployment of production‑grade NLP and Generative AI solutions. You will own complex AI initiatives end‑to‑end, drive technical architecture, and help shape our NLP/LLM engineering practices.

• Lead the design and development of scalable NLP and LLM‑powered solutions.
• 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 robust data, training, inference, and model‑evaluation pipelines.
• Drive improvements in model quality, latency, scalability, reliability, and cost.
• Establish best practices for experimentation, evaluation, deployment, and monitoring of AI systems.
• Translate complex business requirements into effective AI/ML solutions.
• Collaborate closely with ML engineers, software engineers, product teams, and stakeholders.
• Mentor engineers and provide technical leadership across NLP/Generative AI initiatives.
• Stay current with emerging research, models, frameworks, and best practices in NLP and Generative AI.

• 5+ years of professional experience in NLP, Machine Learning, or Generative AI.
• Strong Python programming and hands‑on experience with PyTorch, Tensor Flow, Hugging Face, or equivalent frameworks.
• Deep understanding of NLP, transformers, LLMs, embeddings, RAG, fine‑tuning, and model evaluation.
• Proven 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.

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