AI Solutions Architecht

2060 Digital • Cincinnati, OH

6h

About The Position

Partner with internal stakeholders to translate business needs into AI
platform requirements (performance, latency, cost, governance, and security).
Architect and implement AI-enabling infrastructure, including:
RAG pipelines, embedding services, retrieval orchestration, and evaluation
loops
Vector storage/search (e.g., Postgres + pgvector and/or Pinecone),
including indexing, metadata filtering, and performance tuning
Context/knowledge graphs and metadata frameworks to improve grounding and
reuse
MCP servers and client integrations to connect AI applications to data
Build and maintain ingestion and transformation workflows for AI use cases
(document processing, chunking, embedding generation, batch/real-time
pipelines).
Develop the core services and integrations that power AI-enhanced
applications and agentic tooling (APIs, events/queues, background jobs,
caching, permissions, and system integrations).
Integrate with third party software, cloud storage, and data platforms (e.g.,
Azure Blob Storage, Snowflake and other sources as applicable) to support
ingestion, retrieval, and AI workflows.
Collaborate closely with others to build software and agentic workflows on
top of the platform (tool calling, orchestration, memory/context, and
tool/system connectivity).
Deploy and operate solutions from prototype to production with reliability
practices (monitoring, logging/tracing, alerting, incident readiness, and
cost controls).
Implement secure authentication and authorization mechanisms for data and
tool access.
Help implement AI guardrails and governance controls: access patterns,
auditability, safe data exposure, and safety/quality checks including
human-in-the-loop workflows.
Perform architecture reviews and platform assessments to improve resilience,
security, performance, and maintainability; document standards; provide
technical guidance and enablement across teams.
Stay current on AI infrastructure patterns, tools, and best practices.
Other duties as assigned.

Requirements

• Bachelor's or Master's degree in Computer Science, Information Systems, or a related field (or equivalent experience).
• 5 + years in solutions architecture, platform engineering, data architecture, or similar roles; at least 1-2 years of AI/ML platform experience strongly preferred.
• Demonstrated ability to implement and operate production systems (not just design), including troubleshooting and performance tuning.
• Strong understanding of AI system architecture: RAG/embeddings/vector search, context/knowledge graphs + metadata, model tradeoffs, and agentic workflow enablement (tools/orchestration/memory).
• Experience with relational databases and vector retrieval storage, including Postgres + pgvector (preferred) and/or Pinecone.
• Cloud architecture experience including security, networking/IAM, and cost management; familiarity with Azure services a plus.
• Familiarity with modern delivery/infrastructure tooling (e.g., Docker, CI/CD, and Infrastructure-as-Code).
• Experience with modern data platforms and pipelines, including batch processing (scheduled loads) and streaming (continuous/near real-time) data ingestion into warehouses/lakes.
• Experience building/integrating APIs and services that support applications, automation, and internal tooling
• Strong problem-solving, communication, and cross-functional collaboration skills
• Ability to work in compliance with company policies and procedures.
• Ability to function successfully in team environment.
• Project an appropriate professional appearance and demeanor.
• Ability to work established schedule and other hours as needed.
• Ability to communicate in English both verbally and in writing.
• Ability to sit or stand and work at a computer screen for periods of time.
• Good vision to see computer screen.
• Dexterity to manipulate computer keys and other office equipment
• Requires the ability to think critically, strategically, and to articulate information in a clear and concise manner to others verbally and in writing.
• Work under pressure, meeting tight deadlines.
• Read, hear and speak clearly.
• Prepare reports, business correspondence, and business proposals.
• Think quickly and logically.
• Must be able to perform the essential functions of the job. The Company will make reasonable physical accommodations to facilitate the ability to perform essential job functions.

Nice To Haves

• Familiarity with governance/privacy/responsible AI practices preferred.
• Cloud architecture experience including security, networking/IAM, and cost management; familiarity with Azure services a plus.
• Experience with relational databases and vector retrieval storage, including Postgres + pgvector (preferred) and/or Pinecone.
• 5 + years in solutions architecture, platform engineering, data architecture, or similar roles; at least 1-2 years of AI/ML platform experience strongly preferred.

Responsibilities

• Partner with internal stakeholders to translate business needs into AI platform requirements (performance, latency, cost, governance, and security).
• Architect and implement AI-enabling infrastructure, including: RAG pipelines, embedding services, retrieval orchestration, and evaluation loops, Vector storage/search (e.g., Postgres + pgvector and/or Pinecone), including indexing, metadata filtering, and performance tuning, Context/knowledge graphs and metadata frameworks to improve grounding and reuse, MCP servers and client integrations to connect AI applications to data
• Build and maintain ingestion and transformation workflows for AI use cases (document processing, chunking, embedding generation, batch/real-time pipelines).
• Develop the core services and integrations that power AI-enhanced applications and agentic tooling (APIs, events/queues, background jobs, caching, permissions, and system integrations).
• Integrate with third party software, cloud storage, and data platforms (e.g., Azure Blob Storage, Snowflake and other sources as applicable) to support ingestion, retrieval, and AI workflows.
• Collaborate closely with others to build software and agentic workflows on top of the platform (tool calling, orchestration, memory/context, and tool/system connectivity).
• Deploy and operate solutions from prototype to production with reliability practices (monitoring, logging/tracing, alerting, incident readiness, and cost controls).
• Implement secure authentication and authorization mechanisms for data and tool access.
• Help implement AI guardrails and governance controls: access patterns, auditability, safe data exposure, and safety/quality checks including human-in-the-loop workflows.
• Perform architecture reviews and platform assessments to improve resilience, security, performance, and maintainability; document standards; provide technical guidance and enablement across teams.
• Stay current on AI infrastructure patterns, tools, and best practices.
• Other duties as assigned.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level