We are seeking a dedicated Generative AI Engineer to develop innovative Generative AI solutions in collaboration with our data science and IT teams. Your primary responsibility will be to build Data pipelines (ETL) and Generative AI solutions with robust architecture, effective MLOps/LLMOps processes, and comprehensive controls. Additionally, we are looking for someone with extensive experience in full stack Azure development to ensure seamless integration and deployment of AI solutions.

REQUIRED QUALIFICATIONS:

• GenAI/ML Solution Pods : Experience with leading GenAI/ML Solution Pods for production-ready AI applications.
• Educational Background : Graduate degree in Computer Science, Engineering, or a related field.

PREFERRED QUALIFICATIONS:

• Release Management : Experience with scaled-up release management for AI applications.
• Microservice Architecture : Experience in building microservice-based architecture for GenAI solutions.
• Data Processing : Experience in working with large-scale data sources in complex environments and implementing scalable and efficient data processing pipelines.
• Azure Expertise : Expert knowledge in full stack Azure development, including experience with Azure cloud platforms in the context of GenAI solution development and deployment, and familiarity with common tools in data science.
• Generative AI Knowledge : Solid understanding of generative AI models and algorithms, and their applications in solving complex business problems.
• Communication and Leadership : Strong communication and leadership skills, with the ability to effectively collaborate with multi-functional teams and partners.
• Motivation and Results-Driven : Highly motivated and results-driven, comfortable in handling changes, and delivering results in complex situations.

• Build Solutions: Build ETL data pipelines to ingest, curate and index the data required for AI use cases. Collaborate with Data Scientists to develop and implement generative AI solutions that solve complex business problems and deliver significant business value.
• Multi-functional Collaboration: Work closely with architects to build a high-performance environment where data scientists and engineers can develop and deploy GenAI solutions efficiently.
• Solution Design: Serve as a key member of the solution design group to clearly define technical solutions, project requirements, deliverables, and timelines.
• Stay Updated: Keep abreast of the latest advancements in AI technologies and methodologies, evaluating their potential applications to improve our AI engineering processes.
• Adopt ML Ops Practices: Drive the adoption of ML Ops practices within the GenAI engineering team, ensuring efficient and reliable deployment of GenAI solutions.
• Foster Innovation: Cultivate a culture of innovation, collaboration, and continuous learning within the GenAI engineering team.