GenAI Engineer
Summary
Build and deploy GenAI applications on Azure and AWS, integrating LLMs and AI agents while optimizing models for performance and scalability.
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.