Solutions Architect 3 - 114211-1

Hands-on experience designing Retrieval-Augmented Generation (RAG) architectures, including ingestion pipelines, document preprocessing/chunking, embeddings, vectorization, retrieval, ranking, and context assembly. The Solutions Architect 3 will lead end-to-end architecture for complex enterprise and AI-focused solutions , balancing scalability, performance, security, reliability, and rapid delivery.

Cullerton Group has a new opportunity for a Solutions Architect 3. The work will be 100% remote for candidates residing in the U.S., with preference for candidates in the Chicago or Peoria, IL area. This is a 12-month position that can lead to permanent employment with our client. Compensation is up to $84/hr + full benefits (vision, dental, health insurance, 401k, and holiday pay). The position may require up to 25% travel to Chicago or Peoria.

Job Summary
The Solutions Architect 3 will lead end-to-end architecture for complex enterprise and AI-focused solutions, balancing scalability, performance, security, reliability, and rapid delivery. This role will partner closely with business leaders, product owners, engineering teams, and other architects to translate business requirements into scalable technical designs. The architect will also help establish architectural standards, evaluate emerging technologies, and guide AI solutions from proof of concept through production.
Key Responsibilities

• Own and define solution and platform architectures for large-scale, distributed systems from concept through production.
• Design solutions for scalability, performance, resilience, security, and cost efficiency.
• Partner with business leaders, product owners, engineering managers, and delivery teams to align architecture with business objectives.
• Evaluate and introduce new technologies through proofs of concept and architectural prototypes/spikes.
• Establish architecture standards, patterns, and best practices while providing technical guidance and mentorship to engineering teams.
• Ensure solutions meet security, compliance, and regulatory requirements.
• Create and maintain architecture documentation covering technical decisions, rationale, and trade-offs.
• Support the evolution of platform architecture to improve reliability, developer productivity, and cost efficiency.

Required Qualifications

• Bachelor’s degree and 5+ years of relevant experience in a solutions/platform architecture capacity.
• Strong architectural thinking with the ability to break down complex problems and clearly communicate technical trade-offs.
• Demonstrated technical leadership and ability to guide engineering teams through architectural and implementation decisions.
• Strong communication skills with both technical and non-technical stakeholders.
• Experience translating business and non-functional requirements into scalable technical designs.
• Strong foundation in modern application and platform architecture, including established architectural patterns and standards.
• Experience designing enterprise-scale distributed systems and cloud-native architectures.
• Hands-on knowledge of AWS, Docker, Kubernetes, CI/CD, infrastructure as code, observability, and automated testing.
• Strong programming background in Python and Java.
• Experience with SQL, NoSQL, Snowflake, data modeling, replication, and sharding.
• Strong API architecture/design experience with REST, GraphQL, and/or gRPC.

Preferred Qualifications

• Experience defining enterprise AI reference architectures and standards.
• Proven experience taking AI solutions from proof of concept to scaled production.
• Hands-on experience designing Retrieval-Augmented Generation (RAG) architectures, including ingestion pipelines, document preprocessing/chunking, embeddings, vectorization, retrieval, ranking, and context assembly.
• Experience with vector databases/search layers and similarity search.
• Experience with Agentic AI frameworks and end-to-end AI workflows.
• Knowledge of prompt design/versioning, context and memory patterns, model routing, and fallback strategies.
• Understanding of LLM lifecycle architecture, including model selection, fine-tuning vs. RAG, evaluation, monitoring, and drift detection.
• Experience addressing AI system requirements around latency, cost/token efficiency, security, data privacy, and guardrails.
• Experience integrating AI capabilities into existing enterprise platforms through APIs and event-driven architectures.

Why This Role?
This position offers an opportunity to architect enterprise-scale AI and digital platforms, influence technology strategy, and work with engineering and business teams on emerging technologies. The role provides exposure to modern cloud-native architecture, distributed systems, RAG, Agentic AI, and production-scale LLM solutions while offering the flexibility of remote work within the U.S.