- About Job/Role

As a Solution Architect at Emerson, you will be responsible for designing and guiding the implementation of scalable, high-performance AI/ML architectures.
Collaborate closely with developers, data scientists, engineers, and business customers to ensure AI/ML solutions meet business needs and technological standards.

- Roles and Responsibilities

Design robust and scalable AI/ML architectures that support the development and deployment of machine learning models and AI solutions.
Develop and guide the implementation of end-to-end AI/ML solutions, including model development, data processing, and system integration.
Evaluate and recommend the latest AI/ML technologies, frameworks, and tools to enhance system capabilities and performance.
Collaborate with software engineers and development teams to integrate AI/ML solutions into existing systems and applications.
Work with multi-functional teams to understand requirements and design solutions aligned with business objectives.
Create and maintain comprehensive documentation for AI/ML architectures, including design specifications, deployment procedures, and standard processes.
Participate in regular Scrum events such as Sprint Planning, Sprint Review, and Sprint Retrospective.
Ensure AI/ML systems comply with security standards, regulatory requirements, and data privacy protocols.

- Required Qualification

Bachelor’s degree or equivalent experience in Computer Science, Data Science, Statistics.
Master’s degree or higher preferred.

- Experience

Total 10–15 years of proven experience.
Proven experience in designing and implementing AI/ML architectures with a consistent record of successful projects.
Extensive experience with machine learning frameworks (Go, TensorFlow, PyTorch), programming languages (C#, .Net, NodeJS), and data processing tools.
Strong understanding of system architecture principles, including distributed systems, microservices, and cloud computing.
Experience with Microsoft Azure cloud services and their AI/ML offerings.
Experience with event-handling systems such as Kafka.
Proficiency in data technologies and data engineering practices.

- Skills

Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical customers.
Knowledge of containerization and orchestration technologies (Docker, Kubernetes) preferred.
Familiarity with AI ethics and responsible AI practices.
Experience with AI/ML model versioning and lifecycle management.
Prior experience in product development and working with teams in Scaled Agile Framework (SAFe) is a plus.
Constant learning mindset to stay updated with emerging technologies in AI/ML.

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