Responsibilities
- Design, develop, and implement GenAI solutions that integrate with Azure and AWS platforms.
- Collaborate with cross-functional teams to gather requirements and define project objectives.
- Conduct research and stay up-to-date with the latest advancements in GenAI, Azure, OpenAI, AWS, and Google GenAI technologies.
- Develop and maintain scalable and efficient codebase for GenAI applications.
- Optimize GenAI models and algorithms for performance and accuracy.
- Troubleshoot and debug GenAI applications, ensuring smooth operation and minimal downtime.
- Collaborate with data scientists and machine learning engineers to enhance GenAI capabilities.
- Provide technical guidance and mentorship to junior team members.
- Stay informed about industry trends and best practices in GenAI engineering and cloud service integration.
Requirements
- Bachelor’s or master’s degree in computer science, Engineering, or a related field.
- Proven experience as a GenAI Engineer, with a focus on Azure and OpenAI integration.
- Experience with AWS and Azure GenAI services is preferred.
- Strong programming skills in languages such as Python and/or Java.
- Experience with Azure services, including Azure WebApp services, Azure AI Services, and Azure Functions.
- Experience with Large Language model integration with APIs, building AI agents and working with multi-modal models.
- Understanding of machine learning algorithms and deep learning frameworks.
- Proficiency in data preprocessing, feature engineering, and model evaluation techniques.
- Strong problem-solving and analytical skills.
- Excellent communication and collaboration abilities.
- Ability to work independently and as part of a team in a fast-paced environment.
- Experience with AWS services, such as Amazon ECS, Fargate, Lambda, Dynamo DB, Bedrock etc.
- Good to have knowledge of Google Cloud services, including Google Cloud AI Platform, Google Cloud Functions, and Google Cloud AutoML.
- Experience with cloud-based deployment and scaling of GenAI applications on Azure, AWS, and Google Cloud.
- Knowledge of containerization technologies such as Docker and Kubernetes.
- Familiarity with DevOps practices and tools for CI/CD pipelines.
- Contributions to open-source GenAI or cloud service integration projects.