Python Engineer

Job Summary 

We are seeking a Python Engineer to work on a Generative AI initiative to join our team. The ideal candidate will have a deep understanding of language models, text-to-image, and other generative AI models. They must possess knowledge of Python, and machine learning frameworks. 

Responsibilities 

Develop and implement generative AI models, including LLMs, text-to-image and generative AI models. 

Train and evaluate models using large datasets. 

Troubleshoot and debug code to ensure high-quality results. 

Keep up to date with the latest developments in the field of generative AI and apply them to our projects. 

Collaborate with other engineers, stakeholders and team members to develop innovative solutions. 

Deep understanding of how to scale models and their limitations 

Ability to quickly identify opportunities for model improvement 
 

Requirements 

Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 

5+ years of experience in developing and training AI/ML models. 

Experience with data querying languages like SQL, scripting languages like Python, and/or statistical/mathematical software e.g. R 

Knowledge of state-of-the-art generative AI models such as GPT, LLaMA, Gemini and DALL-E. 

Experience with Cloud infrastructure and Platforms - Azure /GCP/AWS 

Experience with training and evaluating large-scale models on high-performance computing clusters. 

Strong understanding of deep learning, natural language processing, and computer vision. 

Excellent problem-solving skills and ability to work independently and in a team environment. 

Preferred Qualifications: 

Bachelor's or master's degree in computer science, Data Science, Statistics, Math, Physics, or other Science related discipline with course work in AI/ML. 

Demonstrated experience in developing and training AI models  

Strong knowledge of deep learning, natural language processing, and computer vision. 

Experience with scaling up generative models and deploying them in production environments. 

Ability to work collaboratively in a team environment and communicate complex technical concepts to non-technical stakeholders. 

Experience with version control systems like Git. 

Familiarity with cloud-based ML model deployment (e.g., Azure ML, AWS SageMaker).