Lead Engineer - AI/NLP

Job Summary

We are looking for a skilled and detail-oriented Lead Engineer – NLP Solutions to join our team.

Key Responsibilities

• Building and implementing advanced machine learning (ML) and deep learning (DL) algorithms and models.
• Applying different natural language processing (NLP) techniques to problems such as text classification, text summarization, questions and answers, information retrieval, knowledge extraction, and design of conversational bots by using traditional and generative AI techniques.
• Contributing to the design and development of enterprise-grade generative AI applications, including but not limited to advanced RAG, VDB optimization, LLM evaluation, and finetuning.
• Designing and developing a practical and analytical approach while maintaining a focus on aspects such as data quality and availability, feasibility, scalability, and turnaround time.

Required Qualifications

• At least a bachelor’s / master`s degree in computer science, information systems, statistics, mathematics, or a related field.
• Strong understanding of data science processes, such as data investigation, cleaning, minimal viable models, and nuances related to deployment and enhancement.
• About 8-12 years of experience in NLP, ML, and DL.
• More than one year of experience in generative AI application development at the production level.
• Demonstrated ability in developing NLP / ML / DL / generative AI project solutions.
• Hands-on experience and deep theoretical expertise in NLU, NLP, NLG, and common NLP algorithms and DL architectures such as Transformers, BERT, word2vec, FastText, and ELMO.
• Hands-on experience in building and handling product-level delivery of knowledge graphs, ranking algorithms, recommendation engines, etc.
• In-depth knowledge and experience in handling open-source frameworks, such as TensorFlow, PyTorch, and Huggingface Transformers.
• Expert-level programming experience in Python / C++.
• Familiarity with general software design concepts, product development lifecycles, and ML model deployment best practices.
• Experience in analyzing large amounts of user-generated content and processing data in large-scale environments by using cloud infrastructure.
• Proficiency in scraping data from external sources and developing architectures to store and organize the information for generating insights.
• Experience in contributing to open-source software projects.
• Experience and tenacity to go beyond available tools / techniques to design solutions in line with product requirements.
• Ability to communicate with internal and external stakeholders and convey complex information clearly and concisely.
• Understanding of the cultural differences and work styles in a global environment.