AI Solution Architect

Introduction:

The AI Solution Architect will be responsible for designing end-to-end AI and ML architectures, selecting appropriate AI/ML techniques based on business requirements, defining reference architectures, and ensuring scalability, resilience, performance, and cost optimization. Additionally, they will collaborate with data engineers on data pipelines, feature stores, and data governance, implement MLOps and LLMOps practices, integrate AI solutions with enterprise systems, and provide architectural guidance and technical leadership to data scientists and ML engineers.

Responsibilities:

• Design end-to-end AI and ML architectures
• Select appropriate AI/ML techniques based on business requirements
• Evaluate and integrate AI frameworks, tools, and platforms (TensorFlow, PyTorch, MLflow, LangChain, Copilot, etc.)
• Implement MLOps and LLMOps practices for CI/CD, versioning, and observability
• Define reference architectures, design patterns, and best practices for AI platforms
• Architect AI solutions on cloud platforms (Azure, AWS) or hybrid environments
• Collaborate with data engineers on data pipelines, feature stores, and data governance
• Ensure AI solutions integrate with enterprise systems (APIs, ERP, CRM)
• Define AI governance, security, compliance, and responsible AI frameworks
• Work closely with business stakeholders, product owners, and engineering teams
• Provide architectural guidance and technical leadership to data scientists and ML engineers
• Review designs, mentor teams, and contribute to AI strategy and roadmap

Requirements:

Required Skills: Generative Artificial Intelligence (AI), Machine Learning (ML), Amazon Web Services, Cloud Computing, Copilot 

Preferred Skills: Experience with Azure, AI frameworks like TensorFlow and PyTorch, and implementing MLOps practices