AI Solutions Architect

Accountabilities

• Define technical direction, create architectural standards, and support the delivery of advanced AI solutions across enterprise environments.
• Combine strategic thinking with hands-on architecture expertise to ensure AI platforms are scalable, secure, and optimized for real-world usage.
• Review existing AI initiatives with engineering teams, identify improvement opportunities, and help establish a unified AI platform strategy.
• Design reference architectures for multi-agent orchestration, intent classification, routing frameworks, and context management across AI workflows.
• Define strategies for managing AI context, including token optimization, conversation memory, summarization approaches, and information flow between agents.
• Architect Retrieval-Augmented Generation (RAG) solutions, including ingestion pipelines, retrieval strategies, reranking approaches, and quality optimization.
• Establish AI governance practices covering prompt management, testing frameworks, monitoring, version control, and rollback processes.
• Define resiliency patterns for LLM-powered applications, including provider failover, cost management, observability, and graceful degradation strategies.
• Establish AI safety standards related to security, privacy protection, responsible AI practices, and hallucination mitigation.
• Collaborate with technical and business stakeholders to create implementation roadmaps and guide execution priorities.
• Produce architectural documentation, technical recommendations, and strategic guidance to support AI platform development.

Requirements

• Senior technology professional with extensive experience designing and delivering enterprise AI architectures.
• Proven experience designing and deploying multi-agent AI platforms capable of supporting production-scale workloads.
• Strong expertise in conversational AI systems, including session management, memory strategies, and context handling.
• Hands-on experience architecting scalable RAG pipelines, including document processing, retrieval optimization, embeddings, and ranking strategies.
• Experience working with multiple LLM providers such as OpenAI, Claude/Bedrock, Gemini, or open-source models, understanding trade-offs between quality, cost, latency, and reliability.
• Experience designing intent classification and routing systems handling complex user interactions, multi-label classification, and fallback mechanisms.
• Knowledge of real-time and batch machine learning pipelines and the appropriate use cases for each approach.
• Strong cloud architecture experience with platforms such as AWS, GCP, or Azure.
• Experience with AI orchestration frameworks such as LangGraph, LangChain, or LlamaIndex.
• Familiarity with self-hosted model serving technologies and approaches for cost optimization or sensitive workloads.
• Understanding of AI security, governance, compliance, and responsible AI principles.
• Strong communication skills with the ability to translate complex technical concepts into clear recommendations for technical and non-technical audiences.
• Experience creating AI platform standards, architecture guidelines, or engineering best practices is highly desirable.

Benefits

• Full-time remote contract opportunity with required overlap with US time zones.
• Initial 6-month engagement with potential extension.
• Opportunity to influence enterprise AI strategy and platform development.
• Collaboration with experienced engineering, product, data, and architecture teams.
• Opportunity to work on advanced AI systems involving LLMs, multi-agent architectures, and intelligent automation.
• Flexible consulting environment focused on innovation and technical impact.
• Exposure to large-scale AI transformation initiatives and modern cloud technologies.