Senior Full-Stack AI Developer (Generative AI & LLMs)

We seek a highly skilled Full-Stack AI Developer to lead the development of cutting-edge AI solutions, specializing in

generative AI and large language models (LLMs). You will be responsible for building end-to-end applications, from

designing intuitive front end to training and deploying robust backend models.

Responsibilities:

Front-End Development:

Design and implement user-friendly interfaces for AI applications.

Utilize modern web frameworks (e.g., React, Vue.js) to create engaging user experiences.

Optimize front-end performance and responsiveness.

Back-End Development:

Build scalable and efficient backend systems to support AI model deployment.

Design and implement RESTful APIs for seamless communication between front-end and back-end.

Integrate with cloud platforms (e.g., AWS, Azure) for infrastructure management.

AI Model Development:

Train and fine-tune state-of-the-art generative AI and LLM models.

Leverage deep learning frameworks (e.g., TensorFlow, PyTorch) for model development.

Implement efficient inference pipelines for real-time model predictions.

Data Engineering:

Design and maintain robust data pipelines for model training and evaluation.

Preprocess and clean large-scale datasets for optimal model performance.

Implement data versioning and monitoring strategies.

Preferred Qualifications:

Graduate degree in Computer Science, Artificial Intelligence, or a related field.

5+ years of experience in full-stack development.

2+ years of experience in AI model development, particularly with generative AI and LLMs.

Strong understanding of machine learning algorithms and natural language processing.

Proficiency in Python and relevant AI frameworks (e.g., TensorFlow, PyTorch).

Skills:

Excellent communication and collaboration skills.

Ability to work independently and in a team environment.

Passion for staying updated with the latest advancements in AI.

Bonus Points:

Experience in the Biopharma industry.

Experience with model optimization techniques (e.g., quantization, pruning).

Contributions to open-source AI projects.

Publications in relevant AI conferences or journals.