AI Architect (Vertex AI)

Capgemini • New York, NY

2d • $130,000 - $140,000 • Hybrid

About The Position

Capgemini is hiring an AI Architect to own end-to-end architecture for generative AI and agentic solutions on Google Cloud for insurance clients. The role shapes solutions during pre-sales, sets technical direction for delivery teams, and ensures deployed solutions are secure, governed, scalable, and cost-efficient. The position is based in Hartford, Connecticut, with three days per week in the office.

The architect defines reference architectures for retrieval-augmented generation, document intelligence, and multi-agent systems on Vertex AI and the Gemini Enterprise Agent Platform. The role includes selecting models and routing and fallback patterns; designing agent, data, and integration architectures; and owning security, compliance, and LLM operations. The architect also estimates proposal and statement-of-work effort and cloud costs, builds proof-of-concepts, presents to client architecture boards and executives, and mentors technical teams.

The posted base compensation range is $130,000–$140,000. The role may also be eligible for variable incentives, bonuses, or commissions. Capgemini's benefits package for regular, full-time employees may include paid time off, company holidays, personal days, sick leave, medical, dental and vision coverage, retirement savings plans, life and disability insurance, employee assistance programs, and other benefits subject to local policy and eligibility.

Requirements

• 12 or more years of experience in IT.
• At least 5 years in cloud, data, or AI architecture.
• At least 2 years designing production GenAI solutions and Vertex AI.
• Deep hands-on Google Cloud Platform experience, including Gemini Enterprise Agent Platform (Vertex AI), Gemini API, Agent Builder, Cloud Run, GKE, BigQuery, Apigee, IAM, and networking.
• Demonstrated experience delivering production RAG and agentic systems at enterprise scale.
• Python and solution-prototyping skills, with the ability to review engineers' code.
• Experience with LLM evaluation, guardrails, and cost optimization, including context caching, batch prediction, and model tiering.
• Experience with infrastructure as code using Terraform and CI/CD for ML or GenAI.
• Strong stakeholder management, solutioning, and documentation skills, including ADRs and HLD/LLD.
• Google Cloud Professional Cloud Architect certification is required or must be obtained within 3 months.

Nice To Haves

• Insurance or financial services domain experience, including underwriting, submissions intake, claims, COI, or E&S.
• Multi-cloud GenAI experience with Azure OpenAI or AWS Bedrock for comparative solutioning.
• Gemini Enterprise (formerly Agentspace) rollout experience for employee-facing agents.
• Model tuning and distillation experience.
• Experience with the Looker semantic layer for analytics agents.
• Google Cloud Professional Machine Learning Engineer certification.
• Google Cloud Generative AI Leader certification.

Responsibilities

• Define reference architectures for RAG, document intelligence, and multi-agent systems on the Gemini Enterprise Agent Platform (Vertex AI).
• Select models, including Gemini family, Model Garden, and open models, according to accuracy, latency, and cost; establish model-routing and fallback patterns.
• Design agent architectures using ADK, Agent Engine, A2A, and MCP, including tool, memory, and session strategies.
• Design data foundations using BigQuery, Cloud Storage, AlloyDB, Vector Search or Vertex AI Search, Document AI, Dataflow, and Pub/Sub.
• Set integration patterns for policy administration, claims, and underwriting systems, including Guidewire and Duck Creek, using Apigee and event-driven APIs.
• Own security and compliance design, including VPC Service Controls, CMEK, IAM, Sensitive Data Protection, audit logging, and data residency.
• Establish LLMOps practices, including evaluation frameworks, prompt and model versioning, monitoring, drift and hallucination tracking, and human-in-the-loop review.
• Lead Responsible AI reviews covering explainability, bias testing, and model-risk documentation for insurance regulators.
• Estimate proposal and SOW effort and cloud costs for tokens, compute, and storage; build POCs to support winning deals.
• Present to client architecture boards and CXOs.
• Mentor GenAI leads and engineers across programs.
• Shape solutions in pre-sales, set technical direction for delivery teams, and ensure solutions are secure, governed, scalable, and cost-efficient.
• Review engineers' code and prototype solutions in Python.

Benefits

• Paid time off, including vacation of 12–25 days depending on employee grade, company-paid holidays, personal days, and sick leave.
• Medical, dental, and vision coverage, or provincial healthcare coordination in Canada.
• Retirement savings plans, such as 401(k) in the U.S. or RRSP in Canada.
• Life and disability insurance.
• Employee assistance programs.
• Other benefits as provided by local policy and eligibility.
• Possible variable incentives, bonuses, or commissions, depending on the position and applicable laws.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed