Job Description

We are seeking a skilled and experienced Lead NLP Engineer to oversee the development, implementation, and deployment of NLP applications. This role requires an individual who will lead a team of engineers and data scientists while ensuring the continuous improvement of NLP models. The Lead NLP Engineer will be responsible for driving innovation, integrating new AI techniques, and maintaining high-quality standards in project development.

Responsibilities:

• Lead the full lifecycle of NLP applications, including requirements gathering, architecture design, model training, fine-tuning, deployment, and monitoring.
• Provide technical direction, mentorship, and guidance to a team of NLP engineers and data scientists.
• Design, build, and optimize NLP models for use cases such as text classification, named entity recognition (NER), information extraction, question answering, summarization, and conversational AI.
• Stay current with the latest advancements in NLP and AI, and incorporate new techniques and technologies into existing and future products.
• Conduct code reviews to ensure adherence to best practices and high-quality standards.
• Develop and deploy RESTful APIs (Flask, Django, FastAPI).
• Foster a culture of continuous improvement and innovation within the NLP team.
• Collaborate with cross-functional teams to ensure smooth project execution and delivery.

Requirements:

• Proficiency in Python and experience with NLP libraries such as Hugging Face Transformers, spaCy, NLTK, or gensim.
• Experience with deep learning frameworks such as PyTorch or TensorFlow.
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
• Strong understanding of NLP techniques, including transformer-based models (BERT, GPT, T5) and classical methods.
• Experience in developing and deploying RESTful APIs (Flask, Django, FastAPI).
• Strong understanding of distributed processing, threading, multiprocessing, and asynchronous programming.
• Solid understanding of software engineering methodologies, including design patterns.
• Experience with Git and practices ensuring clean, extensible, and testable code.
• Proficient with classical machine learning frameworks (scikit-learn).
• Knowledge of multi-agent workflows and best practices.
• Experience with langchain, langgraph, langsmith, or other related frameworks.