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Job Summary
The Solutions Architect 3 / Principal Digital Architect will lead the architecture of complex AI and digital initiatives, owning end-to-end solution and platform architecture from concept through production. The role will translate business and non-functional requirements into scalable, secure, resilient, and cost-effective technical solutions while providing technical leadership across engineering and architecture teams. The position requires strong expertise in AI architecture, RAG, Agentic AI, enterprise distributed systems, cloud-native technologies, and stakeholder communication.
Key Responsibilities
• Define and own end-to-end solution and platform architectures for large-scale distributed systems from concept through production.
• Design solutions that address scalability, performance, resilience, security, reliability, and cost efficiency.
• Partner with business leaders, product owners, engineering managers, and delivery teams to align architecture with business outcomes.
• Evaluate and introduce emerging technologies through proofs of concept and architectural spikes.
• Establish and enforce architecture standards, patterns, and best practices.
• Provide technical leadership, architectural guidance, and mentorship to engineering teams.
• Ensure solutions meet applicable security, compliance, and regulatory requirements.
• Create and maintain architecture documentation, including design rationale, technical decisions, and trade-offs.
• Continuously improve architecture to increase developer productivity, reliability, and cost efficiency.
• Influence technical decisions across teams without direct authority.
• Define AI reference architectures and enterprise AI standards.
• Evaluate classical machine learning, LLM-based, and non-AI approaches based on business requirements.
• Guide AI solutions from proof of concept through production-scale implementation.
• Design RAG architectures covering data ingestion, document preprocessing, chunking, embeddings, vectorization, retrieval, ranking, and context assembly.
• Design and evaluate vector database and similarity-search solutions, considering performance and cost trade-offs.
• Architect Agentic AI frameworks and end-to-end AI workflows.
• Define approaches for prompt design and versioning, context management, memory, model routing, fallback strategies, and model selection.
• Evaluate fine-tuning, RAG, and hybrid AI approaches based on solution requirements.
• Establish AI evaluation, monitoring, drift detection, guardrails, and security practices.
• Integrate AI capabilities into enterprise platforms through APIs and event-driven architectures.
• Design enterprise-scale distributed systems using modern architectural patterns.
• Apply code-level technical understanding when evaluating and designing solutions.
• Design cloud-native solutions using AWS, Docker, Kubernetes, CI/CD, Infrastructure as Code, observability, and automated testing.
• Define data architectures involving SQL and NoSQL databases, Snowflake, data modeling, data warehouses, replication, and sharding.
• Design REST, GraphQL, and/or gRPC APIs, including versioning, documentation, and distributed-service integration.
• Architect AI systems for performance, latency, token efficiency, cost control, security, data privacy, scalability, reliability, monitoring, and evaluation.
Required Qualifications
• Bachelor's degree.
• 5+ years of experience in an architecture capacity.
• Strong architectural thinking with the ability to decompose complex problems and evaluate technical trade-offs.
• Strong technical leadership skills with the ability to influence teams without direct authority.
• Excellent communication skills with both technical and non-technical stakeholders.
• Strong requirements analysis skills and the ability to translate business and non-functional requirements into technical designs.
• Strong foundation in application and platform architecture using modern architectural patterns and standards.
• Strong experience with AI architecture, including RAG, Agentic AI, or enterprise AI solutions.
• Experience designing enterprise-scale distributed systems.
• Strong programming background in Python and Java.
• Experience with AWS and cloud-native architecture.
• Experience with Docker and Kubernetes.
• Experience with CI/CD, Infrastructure as Code, observability, and automated testing.
• Strong understanding of SQL and NoSQL databases and enterprise data architecture.
• Experience with REST, GraphQL, and/or gRPC API design.
• Strong understanding of AI non-functional requirements, including performance, latency, security, privacy, scalability, reliability, cost, monitoring, and evaluation.
