AI Solutions Architect

Eliassen Group; United States; Remote; USD 138,000 - 172,000; Full time

Benefits offered by this job

Medical Benefits

Dental Benefits

Vision Benefits

401k with company matching

Life insurance

Job summary

Eliassen Group is seeking an AI Solutions Architect to enable product teams to ship AI-native capabilities. You will embed with one solution team at a time, co-building production vertical slices across LLM features, agentic workflows, tool calling, and RAG on top of an AI middleware platform.

You will establish evaluation discipline and coach engineers and PMs to sustain delivery after rotation. For our W2 consultants, a comprehensive benefits package is offered, including Medical, Dental,

Qualifications

• Shipped LLM-powered functionality to real users with exposure to production issues.
• Hands-on evaluation engineering with golden datasets, rubrics, LLM-as-judge, and automated quality gates.
• Agentic architecture experience across tool calling, orchestration, context and memory management, and multi-step workflows.
• RAG and semantic search implementation.
• Azure, cloud-native services, REST APIs, and CI/CD.
• Strong software engineering fundamentals with direct coding contributions.
• Ability to become productive in a new codebase and business within 6 to 8 weeks.
• Clear communication of technical decisions to both engineers and product managers.

Responsibilities

• Co-build production vertical slices with solution team engineers, including LLM features, agentic workflows, tool calling, and RAG integrated with an AI middleware platform.
• Own evaluation engineering with golden datasets, rubrics, LLM-as-judge, hallucination and regression detection, and CI/CD quality gates, and transition ownership to the team.
• Design and build reusable platform capabilities on the AI middleware and guide teams through onboarding.
• Decide when to use generative AI versus deterministic approaches.
• Support discovery and shaping through a Catalyst-style program from pain-point identification to first working slice with middleware integration and baseline evaluations.
• Implement model routing, guardrails, observability, and cost controls.
• Coach engineers and product managers on AI-native software patterns, including agent harness and tool design.
• Produce durable artifacts such as architecture notes, evaluation suites, runbooks, and implementation patterns.

Skills

LLM features

Evaluation engineering

Agentic architecture

Azure OpenAI

Terraform

Langfuse

Observability

CI/CD

Tool calling

Tools

Azure

Terraform

Langfuse

REST APIs

Job description

Description

Remote

Our client is seeking an AI Solutions Architect to enable product teams to ship AI-native capabilities. You will embed with one solution team at a time, co-building production vertical slices across LLM features, agentic workflows, tool calling, and RAG on top of an AI middleware platform. You will establish evaluation discipline, design reusable platform capabilities, and coach engineers and product managers so teams continue to iterate after you rotate. Success is measured by sustained delivery of AI features in production, not by prototypes.

Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance.

Rate: $100.00 to $125.00/hr. w2

Responsibilities

• Co-build production vertical slices with solution team engineers, including LLM features, agentic workflows, tool calling, and RAG integrated with an AI middleware platform.
• Own evaluation engineering with golden datasets, rubrics, LLM-as-judge, hallucination and regression detection, and CI/CD quality gates, and transition ownership to the team.
• Design and build reusable platform capabilities on the AI middleware and guide teams through onboarding.
• Decide when to use generative AI versus deterministic approaches.
• Support discovery and shaping through a Catalyst-style program from pain-point identification to first working slice with middleware integration and baseline evaluations.
• Implement model routing, guardrails, observability, and cost controls.
• Coach engineers and product managers on AI-native software patterns, including agent harness and tool design.
• Produce durable artifacts such as architecture notes, evaluation suites, runbooks, and implementation patterns.

Experience Requirements

• Shipped LLM-powered functionality to real users with exposure to production issues.
• Hands-on evaluation engineering with golden datasets, rubrics, LLM-as-judge, and automated quality gates.
• Agentic architecture experience across tool calling, orchestration, context and memory management, and multi-step workflows.
• RAG and semantic search implementation.
• Azure, cloud-native services, REST APIs, and CI/CD.
• Strong software engineering fundamentals with direct coding contributions.
• Ability to become productive in a new codebase and business within 6 to 8 weeks.
• Clear communication of technical decisions to both engineers and product managers.
• Preferred: Azure OpenAI, Azure Container Apps, Terraform.
• Preferred: LiteLLM or similar routing, Langfuse or similar observability.
• Preferred: Experience embedding with or coaching product teams, including consulting or platform enablement.
• Preferred: Facilitation of discovery or use-case prioritization sessions.
• Preferred: Exposure to financial systems, payments, or Office of the CFO.