Principal Digital Architect

Diversified Services Network, Inc.

. Own and define solution and platform architectures for large-scale, distributed systems from concept through production .

Posted 9/22/2026full-timeRemote • Illinois • United StatesLead💰 $132,000 - $140,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining and evolving solution and platform architectures for large-scale, distributed systems, ensuring compliance with security and regulatory standards. Proficient in translating complex business requirements into scalable technical designs while mentoring engineering teams and establishing architectural best practices.

Highest-signal resume keywords

Solution ArchitectureCloud-Native ArchitecturesAI Reference ArchitecturesDistributed Systems DesignCI/CD and Infrastructure as Code

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Hard Skills

Python ProgrammingJava ProgrammingSQL DatabasesNoSQL DatabasesAWS ServicesDockerKubernetesREST API DesignGraphQL API DesignGRPC API Design

Soft Skills

Influencing Without AuthorityArticulating Technical ConceptsMentorshipProblem DecompositionCollaboration

Tools & Technologies

SnowflakeCI/CD ToolsObservability ToolsAutomated Testing ToolsVector Databases

Industry Keywords

Architectural StandardsScalabilityPerformanceResilienceSecurityComplianceRegulatory RequirementsAI IntegrationEvent-Driven ArchitecturesData Privacy

Tech Stack

Tools & technologies

AssemblyAWSCloudDistributed SystemsDockerGraphQLGRPCJavaKubernetesNoSQLPythonSQL

About the role

Key responsibilities & impact

Own and define solution and platform architectures for large-scale, distributed systems from concept through production
Create architecture meeting standards for scalability, performance, resilience, and security
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
Ensure solutions meet security, compliance, and regulatory requirements
Produce and maintain architecture documentation, including rationale and trade-offs
Evolve platform architecture to improve developer productivity, system reliability, and cost efficiency
Partner with business leaders, product owners, engineering managers, and delivery teams to align architecture with business outcomes

Requirements

What you’ll need

Bachelor's degree with 5+ years of experience in this capacity
Ability to decompose complex problem spaces and develop pragmatic architecture options with clearly articulated trade-offs
Ability to influence without authority and guide teams through architectural decisions and implementation challenges
Ability to clearly articulate complex technical concepts to technical and non-technical stakeholders
Ability to translate business and non-functional requirements into scalable technical designs
Strong foundation in 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 trade-offs between classical ML, LLM-based approaches, and non-AI solutions
Experience taking AI systems from proof of concept to scaled production use
Strong programming background in Python and Java, with ability to reason at code level
Experience designing and building enterprise-scale, distributed systems
Hands-on experience with cloud-native architectures, AWS services, Docker, and Kubernetes
Deep understanding of SQL and NoSQL databases, Snowflake, data modeling, replication, and sharding
Experience with CI/CD, infrastructure as code, observability, and automated testing
Strong REST, GraphQL, and gRPC API design experience, including versioning and documentation
Hands-on experience designing Retrieval Augmented Generation (RAG) architectures
Experience with data ingestion pipelines, document preprocessing, chunking strategies, vectorization, embedding models, retrieval, ranking, and context assembly
Understanding of embedding techniques, similarity search, vector dimensions, chunk size and overlap, latency, recall, and cost trade-offs
Experience with vector databases and search layers
Experience with agentic frameworks
Experience with prompt design and versioning, context management, memory patterns, model routing, and fallback strategies
Knowledge of LLM lifecycle considerations, including model selection, fine-tuning, RAG, hybrid approaches, evaluation, monitoring, and drift detection
Understanding of AI performance, latency, cost controls, token efficiency, security, data privacy, and guardrails
Experience integrating AI capabilities into enterprise platforms via APIs and event-driven architectures
Ability to assess, prototype, and productionize emerging AI technologies

Benefits

Comp & perks

401(k)
Dental insurance
Vision Insurance
Disability insurance
Employee assistance program
Health insurance
Health savings account
Life insurance
Paid time off
Paid Holidays
Full benefits