We are seeking a versatile and highly skilled Generative AI and Data Science Engineer with strong MLOps expertise. This role combines deep technical knowledge in data science and machine learning with a focus on designing and deploying scalable, production-level AI solutions. You will work with cross-functional teams to drive AI/ML projects from research and prototyping through to deployment and maintenance, ensuring model robustness, scalability, and efficiency.
Responsibilities
Generative AI Development and Data Science
- Design, develop, and fine-tune generative AI models for various applications such as natural language processing, image synthesis, and data augmentation.
- Perform exploratory data analysis (EDA) and statistical modeling to identify trends, patterns, and actionable insights.
- Collaborate with data engineering and product teams to create data pipelines for model training, testing, and deployment.
- Apply data science techniques to optimize model performance and address real-world business challenges.
Machine Learning Operations (MLOps)
- Implement MLOps best practices for managing and automating the end-to-end machine learning lifecycle, including model versioning, monitoring, and retraining.
- Build, maintain, and optimize CI/CD pipelines for ML models to streamline development and deployment processes.
- Ensure scalability, robustness, and security of AI/ML systems in production environments.
- Develop tools and frameworks for monitoring model performance and detecting anomalies post-deployment.
Research and Innovation
- Stay current with advancements in generative AI, machine learning, and MLOps technologies and frameworks.
- Identify new methodologies, tools, and technologies that could enhance our AI and data science capabilities.
- Engage in R&D initiatives and collaborate with team members on innovative projects.
Requirements
Educational Background
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field. A PhD is a plus.
Technical Skills
- Proficiency in Python and familiarity with machine learning libraries (e.g., TensorFlow, PyTorch, Keras, scikit-learn).
- Strong understanding of generative AI models (e.g., GANs, VAEs, transformers) and deep learning techniques.
- Experience with MLOps frameworks and tools such as MLflow, Kubeflow, Docker, and CI/CD platforms.
- Knowledge of data science techniques for EDA, feature engineering, statistical modeling, and model evaluation.
- Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) for deploying and scaling AI/ML models.
Soft Skills
- Ability to collaborate effectively across teams and communicate complex technical concepts to non-technical stakeholders.
- Strong problem-solving skills and the ability to innovate in a fast-paced environment.
Preferred Qualifications
- Prior experience in designing and deploying large-scale generative AI models.
- Proficiency in SQL and data visualization tools (e.g., Tableau, Power BI).
- Experience with model interpretability and explainability frameworks.
