NLP Engineer

Role OverviewWe 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.Key ResponsibilitiesLead the design and development of scalable NLP and LLM-powered solutionsArchitect and optimize RAG pipelines, embeddings, vector search, and LLM applicationsFine-tune, evaluate, and optimize transformer-based models and LLMs for production useBuild robust data, training, inference, and model-evaluation pipelinesDrive improvements in model quality, latency, scalability, reliability, and costEstablish best practices for experimentation, evaluation, deployment, and monitoring of AI systemsTranslate complex business requirements into effective AI/ML solutionsCollaborate closely with ML engineers, software engineers, product teams, and stakeholdersMentor engineers and provide technical leadership across NLP/Generative AI initiativesStay current with emerging research, models, frameworks, and best practices in NLP and Generative AIRequirements5+ years of professional experience in NLP, Machine Learning, or Generative AIStrong Python programming and hands-on experience with PyTorch, TensorFlow, Hugging Face, or equivalent frameworksDeep understanding of NLP, transformers, LLMs, embeddings, RAG, fine-tuning, and model evaluationProven experience taking AI/ML solutions from experimentation to productionStrong software engineering and system-design skills, including APIs, data pipelines, and scalable architecturesStrong analytical and problem-solving skills with the ability to independently drive technical initiativesPreferredExperience with PEFT/LoRA, quantization, inference optimization, or distributed model trainingExperience with vector databases, MLOps, cloud platforms, and AI evaluation/observability frameworksExperience building AI agents, multimodal systems, or other advanced Generative AI applicationsContributions to open-source projects, research, publications, or applied AI/ML innovation