Solutions Architect (AI)
Summary
Designs and implements AI/ML and generative AI solutions for enterprise clients, focusing on architecture, LLM/RAG pipelines, and MLOps—collaborating with data and product teams to scale AI from proof-of-concept to production while ensuring security, governance, and cloud platform integration.
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.
