Solution architect Jobs in USA

Solutions Architect

Location-100% remote

Interview-2 rounds virtual

Position's Contributions to Work Group:

As a Principal Digital Architect, you will own the end to end architecture solutions for complex systems-balancing scalability, performance, security, and rapid delivery-while influencing Enterprise technology strategy.

This role requires strong technical depth, architectural judgment, and the ability to translate ambiguous business needs into durable, scalable solutions.

Typical task breakdown:

• 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.

Interaction with team:

• Team consists of 18 folks.
• Work environment:
• 100% remote if they are residing in the US however, preferred location is in Chicago or Peoria area.

Education & Experience Required:

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

Required Technical Skills

• 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
• Consideration for top candidates:
• 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.

AI Related Skills

• 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.