Azure GenAI Architect

Azure GenAI architect (understanding of vector stores and other AI components)

Experience managing offshore teams

Ability to manage backlog and prioritize effectively

Responsibilities:

Use Cases and Strategy: Develop and strategize use cases for GenAI applications in alignment with business objectives.

Executive Presence: Engage with senior stakeholders, presenting GenAI solutions and illustrating their impact.

Data Science Expertise: Harness your strong background in data science to drive insights and foster innovation.

AI/ML Understanding: Demonstrate a deep understanding of AI/ML principles, including GenAI-specific approaches.

Azure Ecosystem: Leverage knowledge of the Azure ecosystem to establish AI practices within organizations.

End-to-End AI Lifecycle: Oversee engagements from ideation through proof of concept (POC) to scaling AI solutions.

MLOPS: Implement best practices for machine learning operations (MLOPS).

Qualifications:

Experience: Minimum of 6-12 years of relevant experience.

Education: Bachelor’s degree required.

Product Management: Excellent product management skills.

Agile/Scrum: Familiarity with Agile/Scrum methodologies.

Cloud Platforms: Knowledge of cloud big data platforms (Azure).

AI/ML: Understanding of AI/ML, including GenAI/LLM solutions.

High level:

• Azure GenAI architect (understanding of vector stores and other AI components)
• Experience managing offshore teams
• Ability to manage backlog and prioritize effectively

Responsibilities:

• Use Cases and Strategy: Develop and strategize use cases for GenAI applications in alignment with business objectives.
• Executive Presence: Engage with senior stakeholders, presenting GenAI solutions and illustrating their impact.
• Data Science Expertise: Harness your strong background in data science to drive insights and foster innovation.
• AI/ML Understanding: Demonstrate a deep understanding of AI/ML principles, including GenAI-specific approaches.
• Azure Ecosystem: Leverage knowledge of the Azure ecosystem to establish AI practices within organizations.
• End-to-End AI Lifecycle: Oversee engagements from ideation through proof of concept (POC) to scaling AI solutions.
• MLOPS: Implement best practices for machine learning operations (MLOPS).

Qualifications:

• Experience: Minimum of 6-12 years of relevant experience.
• Education: Bachelor’s degree required.
• Product Management: Excellent product management skills.
• Agile/Scrum: Familiarity with Agile/Scrum methodologies.
• Cloud Platforms: Knowledge of cloud big data platforms (Azure).
• AI/ML: Understanding of AI/ML, including GenAI/LLM solutions.