This is a remote position.
We are seeking an experienced AI Solutions Architect to design and lead end-to-end AI implementations across enterprise environments. This role focuses on translating business objectives into scalable, secure, and production-ready AI architectures, including LLM applications, retrieval-augmented generation systems, and advanced machine learning platforms.
You will work closely with executive stakeholders, product leaders, data teams, and engineering groups to design AI solutions that are technically sound, operationally scalable, and aligned with long-term business strategy.
This is a leadership-level technical role requiring deep architecture experience, strong cloud expertise, and a practical understanding of AI system deployment.
Key Responsibilities:
• Design end-to-end AI system architectures across AWS, Azure, or GCP
• Architect LLM-based solutions including RAG, embedding, vector search, and tool integrations
• Define scalable inference and model hosting strategies
• Lead design reviews and guide engineering teams through implementation
• Establish governance, security, and compliance standards for AI systems
• Design data pipelines and integration patterns for AI workloads
• Evaluate model providers, frameworks, and cost-performance trade-offs
• Define monitoring strategies for model performance, drift, and reliability
• Present architectural recommendations to technical and non-technical stakeholders
• Drive best practices for AI deployment and lifecycle management
Requirements
Requirements
• 6+ years of experience in solution architecture or senior engineering roles
• Strong cloud architecture experience in AWS, Azure, or GCP
• Proven experience designing distributed systems
• Experience with LLM applications and RAG architectures
• Strong understanding of vector databases and embedding workflows
• Experience integrating AI systems with enterprise APIs and data platforms
• Experience with containerization (Docker) and scalable deployment patterns
• Strong understanding of security, IAM, and data privacy considerations
• Ability to communicate complex technical concepts to executive stakeholders
Advanced / Preferred Qualifications
• Experience designing multi-tenant AI platforms
• Experience implementing governance and AI risk frameworks
• Experience optimizing inference cost and performance
• Experience with MLOps and model lifecycle management
• Background in data engineering or ML engineering
• Experience in regulated industries such as finance or healthcare
Ideal Candidate Profile
The ideal candidate:
• Thinks architecturally before coding
• Balances innovation with production stability
• Understands cost, scalability, and compliance trade-offs
• Has led cross-functional AI initiatives
• Can bridge executive strategy and engineering execution
— Sarah J.
·
Marketing Manager
★★★★★
Verified
