Solutions Architect (AI)

Description The Solution Architect (AI) designs and delivers end-to-end AI, machine learning, and generative AI solutions that integrate cleanly into the enterprise's existing technology landscape. This role translates business use cases into scalable, secure, and governable solution architectures — evaluating platforms and vendors, defining data and MLOps pipelines, and partnering closely with Data Engineering, Data Science, Enterprise Architecture, and Product teams to move AI initiatives from proof-of-concept to production.

• Design end-to-end AI/ML and generative AI solution architectures aligned to business requirements and enterprise architecture standards.
• Translate business use cases into technical solution designs, including LLM integration, RAG (Retrieval-Augmented Generation) pipelines, and ML model deployment.
• Evaluate and select AI/ML platforms, frameworks, and vendors (e.g., Azure AI/OpenAI Service, AWS Bedrock/SageMaker, GCP Vertex AI, open-source LLMs).
• Define data pipelines and MLOps practices for model training, deployment, monitoring, versioning, and retraining.
• Ensure AI solutions comply with data governance, security, and privacy requirements, and align with responsible AI principles.
• Collaborate with Data Engineering, Data Science, Enterprise Architecture, and Product teams to embed AI capabilities into existing systems.
• Build proofs-of-concept and prototypes to validate AI use cases before committing to full-scale implementation.
• Provide technical leadership and mentorship to engineering teams implementing AI solutions.
• Track emerging AI/GenAI technologies and advise leadership on adoption strategy and roadmap prioritization.
• Document solution architectures, integration patterns, and key technical decisions for governance and knowledge continuity.

Requirements

• 8–12+ years in solution or enterprise architecture roles, including 3+ years focused specifically on AI/ML or generative AI solutions.
• Hands-on experience with LLMs, RAG architectures, prompt engineering, and vector databases (e.g., Pinecone, Weaviate, pgvector).
• Practical experience with at least one major cloud AI platform: Azure AI/OpenAI Service, AWS Bedrock/SageMaker, or GCP Vertex AI.
• Solid understanding of MLOps practices — model versioning, CI/CD for ML, monitoring, and automated retraining pipelines.
• Proficiency in Python and familiarity with core ML frameworks (TensorFlow, PyTorch, Hugging Face).
• Strong grounding in data architecture, APIs, microservices, and enterprise integration patterns.
• Working knowledge of responsible AI principles, data privacy regulations (e.g., GDPR), and AI governance frameworks.
• Excellent communication skills — able to translate complex AI concepts for both technical and non-technical stakeholders.
• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
• Cloud AI certification (e.g., Azure AI Engineer Associate, AWS Certified Machine Learning – Specialty, Google Cloud Professional ML Engineer).
• Enterprise architecture certification (e.g., TOGAF), especially if the role will interface closely with the broader EA practice.
• Experience standing up an AI Center of Excellence or AI governance framework from scratch.