NLP Engineer
Job Description & How to Apply Below
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.
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