Solutions Architect 3 / AI

Job Title: Solutions
Architect 3

Location: 100%
Remote. If they are residing in the US however, preferred location is
in Chicago, IL or Peoria, IL area.

Duration: 12+
months contract

This is
Architecture position for AI related projects which require daily
communications with business owners and other members of architecture team
located in US.

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 tradeoffs.

•         Continuously evolve platform architecture to
improve developer productivity, system reliability, and cost efficiency.

Education
& Experience Required:

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

Required
Technical Skills (Required):

•         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.

Disqualifiers/Red
Flags/Overqualifications:

·         Consistent job hopping/ choppy tenure.

·         If candidate is not local but open to relocation
on their dime and being there in office day 1, please make that clear on the
resume.