Solutions Architect - in United

• 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 trade offs.
• Continuously evolve platform architecture to improve developer productivity, system reliability, and cost efficiency.

• Team consists of 18 folks.

• 100% remote if they are residing in the US however, location is in Chicago or Peoria area and expected to be in office 2x days a week.

• Bachelor's degree with 5+ years experience in this capacity

(Required)

• 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.

• 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 architectures, including AWS services, containerization, and orchestration (Docker, Kubernetes).
• Deep understanding of data architecture: SQL and No
  
  SQL databases, data warehouses (Snowflake specifically), data modeling, replication, and sharding.
• Experience with modern Dev Ops 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 (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 product ionize emerging AI technologies aligned to business use cases.