Solution Architect, Agentic AI

Insight Global; United States; On-site; USD 160,000 - 220,000; Full time

Job summary

Insight Global is seeking a Solution Architect to help enterprise organizations turn complex business problems into production-ready AI solutions. This hands-on role blends AI architecture, engineering, consulting, and business strategy, requiring you to work with executives and technical teams to design scalable, secure AI systems.

You will lead discovery, define AI use cases, map data flows, and prototype using Python or TypeScript while guiding architecture decisions across Azure, AWS, and

Qualifications

• Experience designing AI architectures for enterprise-scale deployments.
• Ability to translate business problems into AI use cases and roadmaps.
• Proven track record shipping agents or multi-agent systems to production.

Responsibilities

• Lead discovery sessions with senior stakeholders.
• Translate business problems into AI use cases and system designs.
• Architect enterprise-grade AI agents and multi-agent systems.
• Build working prototypes using Python or TypeScript in real data environments.

Skills

Agentic AI

Python

TypeScript

Cloud Architecture

Consulting

Observability

Education

Bachelor's degree in Computer Science or related field

Tools

AWS

Azure

GCP

APIs

Job description

Compensation: $160,000 to $220,000 base + quarterly bonus

About the Opportunity

We’re looking for a Solution Architect to help enterprise organizations turn complex business problems into production-ready AI solutions.

This role sits at the intersection of AI architecture, hands-on engineering, consulting, and business strategy. You’ll work directly with senior business and technical stakeholders to understand their challenges and identify high-value opportunities for AI.

You’ll then translate those opportunities into solutions that engineering teams can actually build and operate.

This is a hands-on architecture position. You should be comfortable moving from a client discovery session into architecture design and then building a working prototype using Python or TypeScript.

What You’ll Do

• Lead discovery sessions with senior business and technical stakeholders.
• Translate ambiguous business problems into clearly defined AI use cases.
• Map existing processes, systems, data flows, integrations, and constraints.
• Identify and prioritize opportunities where agentic AI can create measurable business value.
• Architect enterprise-grade AI agents and multi-agent systems.
• Design orchestration, tool calling, retrieval, guardrails, human-in-the-loop workflows, evaluation, and observability.
• Integrate agents with enterprise systems through APIs, MCP, and other integration patterns.
• Build working prototypes using Python or TypeScript and real-world data.
• Design backend services, APIs, data models, and enterprise integrations.
• Architect solutions across Azure, AWS, and/or Google Cloud.
• Design for scalability, reliability, security, compliance, and responsible AI.
• Establish evaluation strategies that measure model and agent quality.
• Incorporate monitoring and observability into production AI architectures.
• Evaluate architectural tradeoffs across performance, latency, reliability, and cost.
• Model existing operating costs and quantify potential savings or business impact.
• Scope and estimate implementation efforts for proposals and statements of work.
• Reuse proven frameworks and components where appropriate.
• Hand engineering teams a clear, buildable architecture and remain involved as a technical SME.

What We’re Looking For

You have strong experience in consulting, professional services, or solution architecture, preferably supporting enterprise customers across multiple industries.

You can operate equally well with executives, enterprise architects, engineers, security teams, and business process owners.

Most importantly, you have taken AI beyond demonstrations and proofs of concept.

You’ve helped design and ship agents or multi-agent systems used by real users in production.

You understand what happens after an AI application meets enterprise data, security, integration, reliability, and cost requirements.

Technical Experience

Strong candidates will bring experience across several of these areas:

Agentic AI

• Production AI agents and multi-agent architectures
• Agent orchestration and lifecycle management
• Tool calling and enterprise integrations
• MCP or similar integration protocols
• Retrieval-Augmented Generation
• Guardrails and responsible AI controls
• Agent evaluation and testing
• Tracing, monitoring, and observability
• Failure handling and resilient workflows
• Token, inference, and infrastructure cost management

Software & Architecture

• Python and/or TypeScript
• Backend services and APIs
• Data modeling
• Distributed or event-driven architecture
• Production deployment and scaling

Cloud

• Amazon Web Services

Deep expertise in every cloud is not required. Candidates should have meaningful experience architecting and deploying production solutions in at least one major cloud environment.

• Embeddings
• Vector and similarity search
• Model and agent evaluation
• AI quality measurement

You should understand how production AI systems operate within complex enterprise environments.

That includes:

• Identity and role-based access
• Encryption and data protection
• Auditability
• Security controls
• Compliance considerations
• Responsible AI
• Fault tolerance and resilience
• Observability
• Cost governance

You should be able to explain architectural decisions and tradeoffs clearly to both technical and non-technical audiences.

Business & Consulting Skills

Technical depth is only part of this role.

We’re looking for someone who can enter an ambiguous customer environment and quickly understand:

• How the business process works today
• Which systems and data support that process
• Where the biggest inefficiencies exist
• Whether AI is appropriate for the problem
• What the solution should look like
• What the solution will cost
• What business value the solution could create
• What should be built first

You should be comfortable developing business cases using current cost baselines, expected savings, implementation estimates, and prioritized roadmaps.

What Great Looks Like

You’ll likely thrive here if you:

• Have personally helped ship AI systems into production.
• Can explain exactly how an agentic system was orchestrated, evaluated, monitored, and secured.
• Remain hands‑on enough to build working prototypes.
• Think about cost while designing architecture.
• Know when to reuse an existing component rather than build something new.
• Can challenge technical assumptions constructively.
• Understand both cloud architecture and AI fundamentals
• Can turn an unclear business request into an executable technical design.
• Communicate architecture decisions in straightforward business language.

Why This Role

This opportunity offers significant ownership across the AI delivery lifecycle.

You’ll help determine what should be built, why it matters, how it should work, and how engineering teams can put it into production.

The work spans enterprise architecture, agentic AI, cloud engineering, prototyping, consulting, and business strategy. The organization specifically focuses on closing the gap between deployed AI technology and operating models that produce measurable value.

Compensation & Travel

• Base salary: $160,000 to $220,000
• Additional compensation: Quarterly bonus