Google Cloud AI Engineer

Experis; Hartford (CT); On-site; USD 96,000 - 103,000; Full time

Job summary

Experis in Hartford, CT is seeking a Senior AI Solutions Engineer to lead forward-deployed engagements focused on agentic AI for the insurance industry. This onsite role designs, builds, and scales GenAI systems on Google Cloud to deliver production-ready solutions.

You will drive end-to-end delivery from discovery to deployment, prototype quickly, and establish reusable accelerators and best practices across engagements, working with business and tech stakeholders to achieve measurable

Qualifications

• 5–10 years of experience across solution engineering/AI engineering/forward-deployed engineering
• Hands-on experience building GenAI and Agentic AI on Google Cloud

Responsibilities

• Lead forward-deployed engagements for agentic AI in insurance, from discovery to production deployment
• Design, build, and scale GenAI/Agentic AI solutions on Google Cloud services
• Turn underwriting, claims, pricing, distribution, and servicing challenges into production-ready AI products
• Apply prompt engineering, agent orchestration, RAG architectures, evaluation frameworks, and AI governance

Skills

GenAI & Agentic AI

LLM design

Prompt engineering

Agent orchestration

RAG architectures

AI governance

Client-facing experience

Tools

Google Cloud

Vertex AI

Gemini

BigQuery

Agent Builder

Cloud Run

Job description

Experis is seeking a Senior AI Solutions Engineer to lead forward-deployed engagements focused on agentic AI for the insurance industry. In this Hartford, CT onsite role, you will design, build, scale, and evaluate production-ready GenAI and Agentic AI systems running on Google Cloud.

This position emphasizes end-to-end delivery, from early discovery to production deployment, with rapid prototyping and an approach grounded in strong LLM application design practices.

What you’ll do

• Lead forward-deployed engagements for agentic AI solutions in insurance, progressing from discovery through production deployment.
• Design, build, and scale GenAI and Agentic AI solutions using Google Cloud services including Vertex AI, Gemini, BigQuery, Agent Builder, Cloud Run, and related capabilities.
• Turn underwriting, claims, pricing, distribution, and servicing challenges into production-ready AI products that deliver measurable business outcomes.
• Apply LLM application design practices such as prompt engineering, agent orchestration, RAG architectures, evaluation frameworks, and AI governance.
• Prototype quickly and iterate fast, moving from concept to working demonstrations within days, while contributing reusable accelerators and best practices across engagements.

What you’ll bring

• 5–10 years of experience across solution engineering, AI engineering, forward-deployed engineering, consulting, or enterprise application development, preferably in client-facing roles.
• Hands-on experience building GenAI and Agentic AI solutions on Google Cloud (Vertex AI, Gemini, BigQuery, Agent Builder, Cloud Run, and related services).
• Strong understanding of LLM application design including prompt engineering, agent orchestration, RAG architectures, evaluation frameworks, and AI governance.
• Experience designing and integrating enterprise applications, APIs, data platforms, and workflow solutions.
• Insurance domain expertise in Property & Casualty, Life, Reinsurance, or Specialty Insurance operations, including familiarity with underwriting, claims, pricing, actuarial, policy administration, customer servicing, or distribution/broker operations.

Technology focus

• Google Cloud
• Vertex AI
• Gemini
• BigQuery
• Agent Builder
• Cloud Run
• Prompt engineering
• Agent orchestration
• RAG architectures
• Evaluation frameworks
• AI governance
• APIs

Compensation and location

• Location: Hartford, CT (onsite)
• Pay range: USD 70–75 per hour

What’s in it for you

• Deliver agentic AI solutions designed to create measurable impact across underwriting, claims, and customer-facing workflows.
• Work in a forward-deployed environment collaborating closely with business and technology stakeholders to drive outcomes.
• Build reusable accelerators and best practices that strengthen delivery across multiple engagements.
• Use modern Google Cloud AI capabilities to design, prototype, and deploy production-ready solutions.
• Join a team that values experimentation, rapid iteration, and clear communication with executives and technical teams.