Senior NLP Engineer (contract)

The Senior AI Expert will design, develop, and deploy advanced NLP and LLM-driven solutions that power intelligent search, automation, and decision-support capabilities. This role requires deep expertise across modern NLP techniques, transformer architectures, model optimization, and production-grade ML engineering. The candidate will lead end-to-end solution delivery, from research and prototyping through scalable deployment in cloud environments. Key Responsibilities • Architect, build, and optimize NLP models using Hugging Face Transformers (BERT, GPT, etc.), NLTK, and knowledge-graph-based approaches. • Design and develop semantic search, entity extraction, text classification, summarization, and conversational AI solutions. • Develop production-ready NLP and LLM pipelines using Python, PyTorch, TensorFlow, and Keras. • Lead implementation of retrieval-augmented generation (RAG) for enterprise applications. • Build APIs and microservices to expose NLP/LLM capabilities across products. • Integrate models into cloud platforms (GCP or Azure) for scalable deployment. • Implement MLOps practices including model versioning, CI/CD workflows, automated retraining, and performance monitoring. • Optimize model performance using quantization, pruning, and model compression techniques. • Enable COE teams through reusable components, best practices, and technical mentorship. • Collaborate with cross-functional partners to convert business needs into AI-enabled technical solutions. Must-Have Skills • Deep NLP expertise with transformer-based architectures (Hugging Face, BERT, GPT). • Strong Python development experience for NLP and ML model building. • Hands-on experience with PyTorch, TensorFlow, or Keras. • Experience deploying production-grade NLP solutions. • Knowledge of semantic search, embeddings, knowledge graphs, and text-processing techniques. Good-to-Have Skills • Experience with LLM fine-tuning, prompt engineering, and RAG patterns. • API development using REST and microservices patterns. • CI/CD for ML models using Jenkins, GitHub Actions, or similar. • MLOps practices: model monitoring, versioning, and automated retraining. • Distributed processing for large-scale NLP workloads. • Cloud experience (GCP or Azure). Trainable Skills • Model compression, quantization, and inference optimization. • Docker and Kubernetes for containerized NLP model deployment. Qualifications • 6–10 years of experience in applied NLP, ML engineering, or AI solution development. • Strong background in productionizing ML models at scale. • Proven experience delivering enterprise-grade NLP or LLM systems. • Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or related field. The pay range that the employer in good faith reasonably expects to pay for this position is $42.35/hour - $66.18/hour. Our benefits include medical, dental, vision and retirement benefits. Applications will be accepted on an ongoing basis. Tundra Technical Solutions is among North America’s leading providers of Staffing and Consulting Services. Our success and our clients’ success are built on a foundation of service excellence. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Unincorporated LA County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: client provided property, including hardware (both of which may include data) entrusted to you from theft, loss or damage; return all portable client computer hardware in your possession (including the data contained therein) upon completion of the assignment, and; maintain the confidentiality of client proprietary, confidential, or non-public information. In addition, job duties require access to secure and protected client information technology systems and related data security obligations.