Sr. Lead AI Engineer

Job Description

Job Description

Key Responsibilities :

AI Platform / Solution Support :

Lead the design, development and deployment of AI solutions using Microsoft Azure, Microsoft Fabric, Azure AI Foundry, Copilot Studio and Azure ML platforms.

Provide technical support and troubleshooting for AI platforms to ensure optimal performance and reliability.

Collaborate with Architecture, Engineering and Data science teams to integrate AI models into existing systems.

Generative Use Case & PoC Development :

Develop and implement generative AI use cases and proof-of-concept (PoC) projects.

Work with stakeholders to identify and prioritize generative AI opportunities.

Create and test generative AI models to demonstrate their feasibility and value.

Generative & Operational AI Assessments :

Conduct assessments of generative and operational AI models to ensure their effectiveness and alignment with business goals.

Evaluate AI models for performance, accuracy, and scalability.

Provide recommendations for improving AI models based on assessment results.

Core Generative Platform Development & Support :

Lead the development and support of core generative AI platforms.

Ensure the scalability, reliability, and security of generative AI platforms.

Implement best practices for generative AI platform development and maintenance.

AI Evolving Skillsets & Learning Paths :

Develop and implement learning paths for AI skill development within the organization.

Stay up-to-date with the latest advancements in AI technologies and methodologies.

Provide training and mentorship to team members to enhance their AI skillsets.

AI Governance & Oversight :

Establish and enforce AI governance frameworks and policies.

Ensure compliance with ethical guidelines and regulatory requirements for AI usage.

Monitor and report on AI governance metrics to ensure responsible AI practices.

Leadership & Organizational Development :

Build and lead a high-performing team, fostering engagement, growth, and results-driven execution.

Perform related duties & responsibilities as assigned / requested.

The ideal candidate will have the following competencies and skills :

Leadership and People Development : Proven ability to lead, mentor, and inspire teams, including direct reports, fostering a culture of innovation and driving the successful execution of AI initiatives.

Technical Expertise in AI and Generative Technologies : Advanced knowledge of AI platforms, frameworks, and tools, including Microsoft Azure, Microsoft Fabric, and Azure AI Foundry, combined with programming expertise in Python, R, or similar languages to deliver scalable and reliable solutions.

Strategic Problem Solving and Decision Making : Skilled in analyzing complex challenges, evaluating AI models for performance and scalability, and developing actionable strategies to align AI solutions with business objectives.

Effective Communication and Collaboration : Demonstrated ability to convey complex technical concepts to diverse audiences, collaborate across technical and non-technical teams, and ensure alignment of AI projects with organizational goals.

Governance and Ethical AI Oversight : Expertise in establishing and maintaining AI governance frameworks, ensuring compliance with ethical guidelines and regulatory standards, and promoting responsible AI practices.

Qualifications : Required :

Bachelor’s degree in a related discipline and 8 years’ experience in a related field; or a Master’s degree and 6 years’ experience; or a Ph.D. and 3 years’ experience; or 12 years’ experience in lieu of a degree in a related field

Proven experience as a Sr. Lead AI Engineer with a focus on AI platform / solution support and generative AI.

Strong knowledge of Microsoft Azure, Microsoft Fabric, Azure AI Foundry and Copilot Studio.

Proficiency in programming languages such as Python, R, Java, or C#.

Experience with AI frameworks and libraries (e.g., TensorFlow, PyTorch).

Experience with DevOps practices and tools for continuous integration and deployment.

Preferred :

Experience with other cloud platforms (e.g., AWS, Google Cloud).

Familiarity with MLOps practices and tools.

Experience with Microsoft Power Platform and Power BI.

Knowledge of data engineering and data pipeline development.