Solutions Architect 3 / AI

Job Summary

Required Qualifications

• •Bachelor's degree
• •5+ years' experience in this capacity
• •Architectural Thinking: Ability to decompose complex problem spaces and develop pragmatic architecture options with clearly articulated trade offs
• •Technical Leadership: Influence without authority; guide teams through architectural decisions and implementation challenges
• •Communication: Clearly articulate complex technical concepts to both technical and non-technical stakeholders
• •Requirements Analysis: Translate business and non-functional requirements into scalable technical designs
• •Platform & Application Architecture: Strong foundation in designing modern application and platform architectures using established patterns and standards
• •100% Remote
• •If residing in the US, preferred location is in Chicago, IL or Peoria, IL area
• •12+ months contract
• •Daily communications with business owners and other members of architecture team located in US
• •Own and define solution and platform architectures for large scale, distributed systems from concept through production
• •Create architecture that meets high standards for scalability, performance, resilience, and security
• •Partner closely with business leaders, product owners, engineering managers, and delivery teams to ensure architectural alignment with business outcomes
• •Assess, select, and introduce new technologies, including proof of concept development and architectural spikes
• •Establish and enforce architectural standards, patterns, and best practices across platform teams
• •Provide architectural guidance and mentorship to engineering teams, ensuring high quality implementation
• •Ensure solutions meet security, compliance, and regulatory requirements
• •Produce and maintain clear architecture documentation, including rationale and tradeoffs
• •Continuously evolve platform architecture to improve developer productivity, system reliability, and cost efficiency
• •Experience defining AI reference architectures and standards for enterprise adoption
• •Ability to explain and defend architectural trade offs between classical ML, LLM based approaches, and non-AI solutions
• •Proven experience taking AI systems from proof of concept to scaled production use
• •Strong programming background in Python and Java, with the ability to reason at code level
• •Proven experience designing and building enterprise scale, distributed systems
• •Hands on experience with cloud native architectures, including AWS services, containerization, and orchestration (Docker, Kubernetes)
• •Deep understanding of data architecture: SQL and NoSQL databases, data warehouses (Snowflake specifically), data modeling, replication, and sharding
• •Experience with modern DevOps practices: CI/CD, infrastructure as code, observability, and automated testing
• •Strong API design experience (REST, GraphQL, gRPC), including versioning and documentation
• •Ability to evaluate and introduce emerging technologies aligned to business goals
• •Hands on experience designing Retrieval Augmented Generation (RAG) architectures, including: Data ingestion pipelines, Document preprocessing and chunking strategies, Vectorization and embedding models, Query time retrieval, ranking, and context assembly
• •Deep understanding of embedding techniques, similarity search, and trade offs across: Vector dimensions, Chunk size and overlap, Latency vs. recall vs. cost
• •Experience with vector databases and search layers (e.g., managed or self-hosted vector stores) and their integration into application architectures
• •Experience with Agentic Frameworks
• •Ability to architect end to end AI workflows, including: Prompt design and prompt versioning, Context management and memory patterns, Model routing and fallback strategies
• •Knowledge of LLM lifecycle considerations, including: Model selection (hosted vs. self-hosted), Fine tuning vs. RAG vs. hybrid approaches, Evaluation, monitoring, and drift detection
• •Strong understanding of AI system non-functional requirements, including: Performance and latency optimization, Cost controls and token efficiency, Security, data privacy, and guardrails
• •Experience integrating AI capabilities into existing enterprise platforms via APIs and event driven architectures
• •Ability to assess, prototype, and productionize emerging AI technologies aligned to business use cases

Desired Qualifications

• •If candidate is not local but open to relocation on their dime and being there in office day 1, please make that clear on the resume