Position : GEN AI Architect with Python
Location : Across USA
Duration : 6+ Months
Responsibilities:
· Analyze large and complex datasets to identify patterns, trends, and insights. Develop, test, and implement machine learning models for predictive analytics.
- Develop and implement generative AI models, including LLMs, text-to-image and generative AI models.
- Partner with data engineering teams to define data requirements and ensure data pipelines are optimized for analysis.
- Conduct hypothesis testing, A/B testing, regression analysis, and other statistical methods to validate business assumptions.
- Collaborate with engineering teams to deploy machine learning models into production and ensure model performance meets business needs.
- Collaborate with other engineers, stakeholders and team members to develop innovative solutions.
- Create compelling visualizations and dashboards using tools like Tableau, Power BI, or similar to present insights to both technical and non-technical audiences.
- 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.
- 5+ Years of experience with Python.
- Experience with data querying languages like SQL, scripting languages like Python, and/or statistical/mathematical software e.g. R
- Knowledge of machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn.
- 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 data visualization tools like Tableau, Power BI, or similar.
- Strong understanding of deep learning, natural language processing, and computer vision.
- 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.
- Experience with version control systems like Git.
- Familiarity with cloud-based ML model deployment (e.g., Azure ML, AWS SageMaker).