Lead Analyst

About the Role: Lead Analyst - AI Engineer (Full Stack) at Eaton

Eaton is seeking a highly skilled Lead Analyst - AI Engineer with full-stack capabilities to join our innovative team. This role is pivotal in developing and deploying cutting-edge AI solutions that drive significant business benefits.

Key Responsibilities:

• Develop and maintain both front-end and back-end components for AI applications.
• Build and consume APIs, create user-centric interfaces, and integrate with other systems using .Net, JavaScript, Angular, React, and SQL.
• Deploy AI solutions by packaging trained models as services using Azure deployment services, Kubernetes, and GitHub for version control.
• Leverage in-depth knowledge of Azure AI services like Azure Machine Learning and Cognitive Services, alongside other AI technologies such as Natural Language Processing (NLP), Computer Vision, and GPT models.
• Collaborate with solution architects and enterprise architects to articulate and document robust solution architectures for AI projects.
• Engage with business leaders to understand their challenges and identify opportunities for AI-driven improvements.
• Utilize strong analytical and problem-solving skills to translate business requirements into effective AI solutions.
• Communicate complex AI technical concepts clearly to both technical and non-technical stakeholders.

Qualifications:

• Bachelor's degree in Engineering (B.E./B.Tech) or equivalent MCA.
• A minimum of 10 years of overall experience, with at least 5 years focused on the end-to-end solutioning and deployment of AI projects.
• Proven technical expertise in .NET, JavaScript, Angular or React, SQL Server, Azure deployment services, Kubernetes, GitHub, and Azure Cognitive Services. Experience with GPT models is essential, and GitHub Copilot is a plus.

Required Skills:

• Proficiency in front-end development frameworks (React or Angular) and back-end technologies (e.g., Node.js, Python, .NET).
• Strong understanding of AI and machine learning frameworks such as TensorFlow and PyTorch.
• Experience with cloud and hybrid infrastructure environments.
• Proficiency in scripting and automation tools like PowerShell and Azure CLI.
• Expertise in cloud-native development, with a specialization in the Microsoft Azure tech stack.