About the job
Compensation: DOE
Role Overview
We are seeking a skilled Generative AI Engineer with a strong foundation in traditional Natural Language Processing (NLP) techniques and deep experience in building and deploying large language models (LLMs). This role requires a balance of expertise in state-of-the-art generative AI and traditional machine learning (ML) methods to drive innovative AI solutions.
Responsibilities
Model Development:
- Develop and fine-tune LLMs using frameworks such as OpenAI, Hugging Face, or Google JAX.
- Build and improve text generation, summarization, question answering, and dialogue generation models.
- Utilize traditional NLP techniques, including tokenization, word embeddings, and feature engineering for enhanced model performance.
Research & Experimentation:
- Stay updated with the latest advancements in generative AI, NLP, and ML, applying this knowledge to create scalable, production-ready models.
- Conduct experiments to assess and benchmark model performance on tasks such as sentiment analysis, text classification, entity recognition, and language understanding.
Model Deployment & Optimization:
- Implement and optimize LLMs and NLP models in production, using best practices for model serving, scaling, and API integration.
- Work with tools such as TensorFlow Serving, TorchServe, and containerized environments (Docker, Kubernetes) for efficient model deployment.
Data Collection & Preparation:
- Design pipelines for data preprocessing, augmentation, and transformation.
- Collaborate with data engineering teams to ensure the availability and quality of datasets for model training.
Collaboration & Communication:
- Collaborate with product managers, data scientists, and other engineering teams to translate business requirements into technical implementations.
- Communicate technical concepts effectively to both technical and non-technical stakeholders.
Requirements
Education:
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, or a related field.
Experience:
- 3-5 years of hands-on experience in developing and deploying NLP models, including experience with at least one modern LLM platform (e.g., GPT, BERT, T5, LLaMA).
- Demonstrated expertise in traditional NLP approaches and techniques (e.g., TF-IDF, CRF, POS tagging).
Technical Skills:
- Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Hugging Face Transformers).
- Strong grasp of model fine-tuning, transfer learning, and prompt engineering techniques.
- Experience with cloud platforms (AWS, GCP, Azure) for model deployment and scaling.
- Knowledge of modern NLP libraries and frameworks, including spaCy, NLTK, and OpenNLP.
- Familiarity with MLOps practices and tools such as MLflow, Airflow, and DVC for experiment tracking and model management.
Soft Skills:
- Strong problem-solving and critical-thinking skills.
- Ability to work in an agile, fast-paced environment with minimal supervision.
- Excellent communication and teamwork abilities.
Preferred Qualifications
- Experience with multimodal AI and integrating text with other data types (e.g., images, audio).
- Knowledge of prompt tuning, reinforcement learning from human feedback (RLHF), and continuous learning techniques for LLMs.
- Contributions to open-source AI/NLP projects or published research in the field of generative AI.
