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Part Time Ai Solution Architect Jobs (NOW HIRING)

GPI Enterprises Inc.

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AI Delivery Lead Architect

Columbus, OH · On-site

$75 - $80/hr

Solution Architecture * Define target architectures for AI products, including agent design, retrieval strategy, model selection, integration points, and data flows, in partnership with senior ...

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Architecture Expert

$65 - $90/hr

... reliable AI solutions for architectural applications. This is a flexible, fully remote, part-time opportunity requiring approximately 10 hours per week , with the potential for long-term ...

Davenport Group

Solutions Architect

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$71.75 - $94.50/hr

Lead Solutions Architect Last Revision Date: 6/6/2024 Full-Time Part-Time Exempt Nonexempt SUMMARY The primary role of this position is solutions design for enterprise data center projects. The ...

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Part Time Ai Solution Architect information

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How much do part time ai solution architect jobs pay per hour?

As of Aug 13, 2026, the average hourly pay for part time ai solution architect in the United States is $70.17, according to ZipRecruiter salary data. Most workers in this role earn between $60.58 and $79.81 per hour, depending on experience, location, and employer.

What is the difference between Part Time Ai Solution Architect vs Part Time Data Scientist?

AspectPart Time Ai Solution ArchitectPart Time Data Scientist
Required CredentialsTypically a degree in computer science, AI, or related field; certifications in AI/ML are commonDegree in data science, statistics, or related; certifications in data analysis or machine learning are common
Work EnvironmentDesigning AI solutions, collaborating with developers, client-facing rolesAnalyzing data, building models, reporting insights, often in research or analytics teams
Employer & Industry UsageTech companies, consulting firms, industries adopting AI solutionsFinance, healthcare, marketing, research institutions

While both roles involve AI and data, the Part Time Ai Solution Architect focuses on designing and implementing AI solutions, whereas the Part Time Data Scientist emphasizes analyzing data and building models. Their skills and daily tasks differ, but both are essential in AI-driven projects.

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Part Time Ai Solution Architect jobs near you
Infographic showing various Part Time Ai Solution Architect job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution, with an average salary of $145,963 per year, or $70.2 per hour.

AI Delivery Lead Architect

GPI Enterprises Inc.

Columbus, OH • On-site

$75 - $80/hr

Part-time

Medical, Dental, Vision, Retirement, PTO

This job post has expired today. Applications are no longer accepted.

Job description

AI Delivery Lead Architect

 

Role Overview

We are seeking an AI Delivery Lead Architect to own the path from business problems to shipped, adopted AI products. This role serves as the single point of accountability between business stakeholders and the engineering team. The AI Delivery Lead is responsible for shaping what gets built, defining what ‘done’ means, and ensuring delivered solutions produce measurable value.

The ideal candidate is technically credible enough to help design solution architecture and challenge engineering decisions, while commercially fluent enough to negotiate scope with executives. This is a leadership and delivery role, but strong depth in AI system design is essential.


Work Hours

Hybrid remote with 15-20% On-Site

Hours per day: 2

Days per week: 5

 

Key Responsibilities

 

Demand Shaping and Prioritization

  • Run intake for AI use-case requests; assess each for business value, feasibility, data readiness, and risk; and maintain a prioritized delivery roadmap.
  • Translate ambiguous business problems into scoped requirements, success criteria, and technical designs that the engineering team can execute.

 

Solution Architecture

  • Define target architectures for AI products, including agent design, retrieval strategy, model selection, integration points, and data flows, in partnership with senior engineers.
  • Set and enforce architectural standards, reusable patterns, and build-versus-buy decisions across the AI portfolio.
  • Evaluate models, platforms, and vendors, and own the technical rationale behind each selection.

 

Delivery Ownership

  • Own the full delivery lifecycle: scoping, estimation, sprint planning, dependency management, risk mitigation, release, and hypercare.
  • Define acceptance criteria appropriate for probabilistic systems, including evaluation sets, accuracy and quality thresholds, latency budgets, and fallback behavior. AI features cannot be accepted through binary pass/fail criteria alone.
  • Actively manage delivery risk, escalate issues early, and keep commitments realistic relative to engineering capacity.

 

Stakeholder and Governance Management

  • Serve as the primary interface for business sponsors. Responsibilities include running discovery sessions, demos, steering reviews, and executive status reporting.
  • Calibrate stakeholder expectations regarding what current AI can and cannot reliably do, and manage the gap between demonstrations and production performance.
  • Guide solutions through security, legal, privacy, and responsible-AI reviews, while maintaining documentation of model use, data handling, and approved use cases.

 

Value Realization

  • Define and track benefit metrics, including adoption, time saved, quality improvements, and costs avoided; report outcomes against the original business case.
  • Own AI platform and inference cost management, including budget forecasting and per-workload cost attribution.
  • Partner with enablement and change-management teams to drive adoption after launch.

Preferred Qualifications

  • Prior hands-on engineering or data experience.
  • Experience with Azure AI Foundry, AWS Bedrock, Google Vertex AI, or comparable enterprise AI platforms.
  • Familiarity with AI governance frameworks.
  • Experience managing vendor relationships and negotiating commercial terms.
  • Product-management experience or formal certification in Agile, PMP, or an architecture framework such as TOGAF.
  • Experience building an AI delivery function from an early-stage or ad hoc state.