Job responsibilities
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Creates secure and high-quality production code, maintaining algorithms that integrate seamlessly with generative AI systems and other appropriate technologies.
- Produces architecture and design artifacts for complex generative AI applications, ensuring design constraints are met by software code development.
- Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets to continuously improve generative AI applications and systems.
- Proactively identifies hidden problems and patterns in data, using these insights to drive improvements in coding hygiene and system architecture specific to generative AI.
- Contributes to software engineering communities of practice and events that explore new and emerging generative AI technologies.
- Fosters a team culture of diversity, equity, inclusion, and respect.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience.
- Proficient in coding in one or more languages, with a strong emphasis on Python and AI/ML frameworks.
- Experience in developing, debugging, and maintaining code in a large corporate environment, utilizing modern programming languages and database querying languages.
- Comprehensive knowledge of the Software Development Life Cycle, with a focus on generative AI applications.
- Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security, particularly in the context of AI/ML.
- Demonstrated knowledge of software applications and technical processes within technical disciplines such as cloud computing, artificial intelligence, machine learning, and mobile technologies.
- Expertise in AWS and GenAI/ML technologies.
Preferred qualifications, capabilities, and skills
- Experience with AI model optimization and performance tuning to ensure efficient and scalable AI solutions.
- Familiarity with data engineering practices to support AI model training and deployment.
- Ability to collaborate with cross-functional teams to integrate generative AI solutions into broader business processes and applications.
- Strong understanding of machine learning algorithms and techniques, including supervised, unsupervised, and reinforcement learning.
- Experience with AI/ML libraries and tools such as TensorFlow, PyTorch, Scikit-learn, and Keras.