Solutions Architect

This is a fully remote position, open to applicants in Illinois.

📋 Description

• Take ownership of comprehensive architectural solutions for intricate systems, ensuring a balance of scalability, performance, security, and swift delivery.

• Shape enterprise technology strategies effectively.

• Outline solution and platform architectures for extensive distributed systems from initial concept to full production.

• Develop architectures that adhere to standards concerning scalability, performance, resilience, and security.

• Collaborate with business leaders, product owners, engineering managers, and delivery teams to ensure architecture aligns with business objectives.

• Evaluate, select, and implement technologies through proof-of-concept initiatives and architectural explorations.

• Set and uphold architectural standards, patterns, and best practices across platform teams.

• Provide architectural guidance and mentorship to engineering teams.

• Ensure that solutions comply with security, regulatory, and compliance standards.

• Generate and maintain architecture documentation, including rationale and trade-offs.

• Advance platform architecture to enhance developer efficiency, system reliability, and cost-effectiveness.

• Design AI-related projects and engage daily with US-based business owners and architecture team members.

⛳️ Requirements

• Candidates must be located in the US, with a preference for those in the Chicago or Peoria, IL area.

• Local candidates are preferred; those from outside the area should be willing to relocate or travel as necessary.

• Significant preference for candidates with experience in the telematics or automotive sectors.

• Minimum of 5–6 years of experience in AI Architecture.

• Practical experience in designing and managing AI solutions at an architectural level.

• Strong background in RAG and Generative AI.

• Bachelor's degree with a minimum of 5 years of relevant experience.

• Capability to break down complex problem spaces and devise practical architectural options with clearly defined trade-offs.

• Ability to influence without direct authority and guide teams through architectural decisions and implementation hurdles.

• Proficiency in articulating complex technical concepts to both technical and non-technical stakeholders.

• Ability to translate business and non-functional requirements into scalable technical designs.

• Solid foundation in modern application and platform architectures utilizing established patterns and standards.

• Experience in defining AI reference architectures and standards for enterprise integration.

• Ability to explain and justify trade-offs between traditional ML, LLM-based methods, and non-AI solutions.

• Proven track record of advancing AI systems from proof of concept to scaled production.

• Strong programming expertise in Python and Java.

• Experience in designing and constructing enterprise-scale distributed systems.

• Practical experience with cloud-native architectures, AWS services, Docker, and Kubernetes.

• In-depth understanding of SQL and NoSQL databases, Snowflake, data modeling, replication, and sharding.

• Familiarity with CI/CD, infrastructure as code, observability, and automated testing.

• Extensive API design experience with REST, GraphQL, and gRPC, including versioning and documentation.

• Ability to assess and incorporate emerging technologies in alignment with business objectives.

• Hands-on experience in designing RAG architectures, covering data ingestion, document preprocessing, chunking, vectorization, embeddings, retrieval, ranking, and context assembly.

• Knowledge of embedding techniques, similarity search, vector dimensions, chunk size and overlap, and latency/recall/cost trade-offs.

• Experience with vector databases and search layers.

• Familiarity with agentic frameworks.

• Capability to architect comprehensive AI workflows, including prompt design/versioning, context management, memory patterns, model routing, and fallback strategies.

• Knowledge of LLM lifecycle considerations, including model selection, fine-tuning versus RAG versus hybrid approaches, evaluation, monitoring, and drift detection.

• Understanding of non-functional requirements for AI systems, such as performance, latency, cost control, token efficiency, security, data privacy, and guardrails.

• Experience in integrating AI capabilities into enterprise platforms via APIs and event-driven architectures.

• Ability to evaluate, prototype, and implement emerging AI technologies.

🏝️ Benefits

• 100% remote work for candidates based in the US.

• 12-month contract.