Principal Digital Architect - Enterprise Scale Systems & AI

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 tea

m:- Team consists of 18 folk

s.
Work environme

nt:100% remote if they are residing in the US however, preferred location is in Chicago or Peoria ar

ea.
Education & Experience Requi

red:- Bachelor’s degree with 5+ years experience in this capa

c

ity Required Technical Ski

lls (Requi

red)• Architectural Thinking: Ability to decompose complex problem spaces and develop pragmatic architecture options with clearly articulated trade o

ffs.• Technical Leadership: Influence without authority; guide teams through architectural decisions and implementation challen

ges.• Communication: Clearly articulate complex technical concepts to both technical and non technical stakehold

ers.• Requirements Analysis: Translate business and non functional requirements into scalable technical desi

gns.• Platform & Application Architecture: Strong foundation in designing modern application and platform architectures using established patterns and stand

ards
Consideration for top candid

ates:• Experience defining AI reference architectures and standards for enterprise adop

tion.• Ability to explain and defend architectural trade offs between classical ML, LLM based approaches, and non AI solut

ions.• 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 l

evel.• Proven experience designing and building enterprise scale, distributed sys

tems.• Hands on experience with cloud native architectures, including AWS services, containerization, and orchestration (Docker, Kuberne

tes).• Deep understanding of data architecture: SQL and NoSQL databases, data warehouses (Snowflake specifically), data modeling, replication, and shar

ding.• Experience with modern DevOps practices: CI/CD, infrastructure as code, observability, and automated tes

ting.• Strong API design experience (REST, GraphQL, gRPC), including versioning and documenta

tion.• Ability to evaluate and introduce emerging technologies aligned to business g

oals.
AI Related

SkillsHands on experience designing Retrieval Augmented Generation (RAG) architectures, incl

uding:Data ingestion pip

elinesDocument preprocessing and chunking stra

tegiesVectorization and embedding

modelsQuery time retrieval, ranking, and context as

semblyDeep understanding of embedding techniques, similarity search, and trade offs a

cross:Vector dime

nsionsChunk size and o

verlapLatency vs. recall vs

. costExperience with vector databases and search layers (e.g., managed or self hosted vector stores) and their integration into application architec

tures.Experience with Agentic Fram

eworksAbility to architect end to end AI workflows, incl

uding:Prompt design and prompt vers

ioningContext management and memory pa

tternsModel routing and fallback stra

tegiesKnowledge of LLM lifecycle considerations, incl

uding:Model selection (hosted vs. self h

osted)Fine tuning vs. RAG vs. hybrid appr

oachesEvaluation, monitoring, and drift det

ectionStrong understanding of AI system non functional requirements, incl

uding:Performance and latency optimi

zationCost controls and token effi

ciencySecurity, data privacy, and guar

drailsExperience integrating AI capabilities into existing enterprise platforms via APIs and event driven architec

tures.Ability to assess, prototype, and productionize emerging AI technologies aligned to business use