Generative AI Engineer

Minimum Qualifications:

• Bachelor's/master's degree in computer science, Engineering, Mathematics or related field.
• 6-8 years of total experience in data science, machine learning or NLP with a significant focus on unstructured data analytics.
• 3+ years of hands-on experience in developing and deploying NLP solutions in an enterprise setting using traditional NLP techniques (non-LLM & non-GenAI based).
• 1+ years of hands-on experience in doing impactful POCs and building LLM based applications.
• Experience in Conversational AI, Information retrieval, Knowledge Management will be a plus.
• Good working knowledge of prompt engineering and RAG based applications.
• Experience with OpenAI LLMs and conceptual idea on open source LLMs.
• Ability to propose and visualize overall solution for a given requirements beyond NLP components.
• Proficient in Python and common NLP libraries, such as NLTK, spaCy, transformers, etc. Ability to write production-grade python code is a plus.
• Familiar with cloud platforms, such as Azure and NLP frameworks, such as Hugging Face, AllenNLP, etc.

Preferred qualifications:

• Experience with the technical aspects of analytics, data science, and statistical methodologies, data management, as well as analytical and data visualization software tools.
• Experience working in an enterprise setting, with knowledge of Data Management market segment and competitive dynamics/customer adoption behavior.
• Excellent communication skills, with the ability to communicate with both technical and business teams.
• Excellent leadership skills with the ability to motivate a team.
• Experience with programming languages such as Python, R, and SQL
• Strong analytical and problem-solving skills
• Excellent written and verbal communication skills

Responsibilities

• Prototype and validate NLP solutions using agile methodologies and best practices. Conduct exploration and development on various NLP topics, such as text classification, named entity recognition, sentiment analysis, question answering, topic classification summarization, etc.
• Design, implement, and evaluate POCs and end-to-end solutions using OpenAI LLMs and other open source LLMs.
• Optimize and fine-tune the performance of LLMs using prompt engineering and RAG-based methods.
• Deploy and monitor NLP models and applications in production environments.
• Communicate and present NLP results and insights to internal and external stakeholders.
• Collaborate with other AI team members and stakeholders to understand the requirements, propose solutions, and deliver high-quality results.
• Communicate effectively with the business partners and present the solutions in a clear and concise manner.
• Stay updated with the latest developments and trends in NLP and LLMs.