What are the responsibilities and job description for the AI Engineer position at Global Applications Solutions?
Job Details
Job Summary:
This role focuses on creating advanced AI applications, including Generative AI systems like chatbots, data retrieval platforms, and analytics tools. The ideal candidate will bring expertise in Python development, a robust understanding of AI/ML technologies, and the ability to seamlessly integrate on-premises infrastructure with Azure/AWS AI cloud services.
Key Responsibilities:
- Collaborate with cross-functional teams to conceptualize, design, and implement AI-powered solutions.
- Develop and maintain end-to-end software applications using Python and other modern frameworks.
- Design and implement scalable APIs and backend services for Generative AI applications such as chatbots and data analytics platforms.
- Build responsive and intuitive front-end interfaces using modern JavaScript frameworks (React, Angular, or Vue.js).
- Develop and integrate machine learning models into applications, ensuring performance and scalability.
- Leverage cloud platforms (Azure, AWS) to deploy AI models, ensuring secure and efficient integration with on-premises systems.
- Drive the adoption of DevOps practices, including CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes).
- Ensure code quality and maintainability through unit testing, code reviews, and adherence to best practices.
- Troubleshoot and resolve complex technical challenges in real-time AI applications.
- Stay updated on emerging AI/ML and full-stack development trends to incorporate innovative solutions.
Qualifications:
- Education: Bachelor s or master s degree in computer science, Software Engineering, or a related field.
- Experience: Minimum 6 8 years of professional experience in full-stack development, with a focus on Python and AI/ML technologies.
- Strong expertise in Python frameworks (Django, Flask, FastAPI) and front-end frameworks (React, Angular, Vue.js).
- Hands-on experience with machine learning frameworks and libraries, such as TensorFlow, PyTorch, Scikit-learn, or Hugging Face.
- Proficiency in developing and deploying AI/ML models in cloud environments like Azure AI, AWS SageMaker, or Google AI Platform.
- Deep understanding of integrating on-premises systems with cloud-based AI solutions.
- Experience with relational and NoSQL databases (PostgreSQL, MySQL, MongoDB).
- Familiarity with containerization (Docker) and orchestration tools (Kubernetes).
- Knowledge of CI/CD pipelines and infrastructure as code tools (Jenkins, Terraform, or Ansible).
- Strong problem-solving skills and the ability to work in fast-paced, dynamic environments.
Preferred Skills:
- Experience with Natural Language Processing (NLP) and Generative AI models like GPT, BERT, or similar.
- Knowledge of big data tools like Apache Spark or Hadoop.
- Familiarity with microservices architecture.
- Certifications in cloud platforms (Azure, AWS) or AI/ML tools.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
