Hiring Senior GCP/AI Engineer (Agentic AI) in Phoenix

BrightSol AI is a US-based enterprise AI solutions firm specializing in the design, deployment, and maintenance of custom agentic AI systems for clients across healthcare, logistics, and financial services. With a growing client base of 120+ mid-sized to enterprise organizations, we are expanding our Phoenix engineering hub to support increased demand for scalable, high-performance AI agent deployments on Google Cloud Platform. Our team of 60+ engineers and AI specialists prioritizes practical, business-impactful AI solutions over experimental hype, with 92% of our deployed agentic AI systems delivering measurable ROI for clients within 6 months of launch.

We are currently seeking a Senior GCP/AI Engineer (Agentic AI) to join our Phoenix-based engineering team. In this role, you will own the end-to-end lifecycle of agentic AI system deployments for our enterprise clients, from initial technical scoping to ongoing optimization and support. You will work closely with our data science, backend engineering, and client success teams to translate complex business requirements into robust, scalable AI agent architectures that meet strict uptime, accuracy, and performance SLAs.

Key responsibilities for this role include:
1. Design and deploy end-to-end agentic AI systems using GCP Vertex AI, LangChain, LlamaIndex, and CrewAI, ensuring 95%+ task completion accuracy for client use cases including customer support automation, supply chain predictive maintenance alerts, and financial document processing workflows.
2. Optimize GCP cloud infrastructure (Compute Engine, Cloud Run, BigQuery, Cloud Storage) tailored for high-throughput AI workloads, targeting a 30% reduction in inference latency and 15% reduction in monthly cloud spend through resource right-sizing, autoscaling configuration, and reserved instance planning.
3. Build and maintain CI/CD pipelines for AI model training, testing, and deployment using GitHub Actions, Cloud Build, and Terraform, reducing new agent feature deployment cycle time from 2 weeks to 3 days for core product updates.
4. Collaborate with cross-functional teams of data scientists, backend engineers, and client stakeholders to translate business requirements into technical AI agent specifications, delivering a minimum of 4 production-ready agentic AI solutions per quarter for active client engagements.
5. Implement comprehensive monitoring, logging, and alerting for AI agent performance using Cloud Monitoring, Prometheus, and custom logging solutions, resolving 90% of critical agent performance issues within 1 hour of detection to meet 99.9% uptime SLA commitments for client deployments.
6. Develop reusable integration layers for agentic AI systems to connect with client existing tech stacks including Salesforce, SAP S/4HANA, ServiceNow, and Zendesk, reducing integration development time by 25% for new client onboarding projects.
7. Conduct code reviews for junior and mid-level engineers on the team, mentor 2-3 team members on GCP AI and agentic AI best practices, and contribute to internal knowledge base documentation, improving overall team delivery efficiency by 20% over a 6-month period.
8. Stay up to date with the latest GCP AI service releases and agentic AI framework updates, piloting a minimum of 2 new GCP AI features per quarter to enhance our core AI agent product offerings and maintain a competitive edge in the market.

Our Phoenix team operates on a hybrid work model, with 3 days per week in our downtown Phoenix office located near the Central Avenue business district, and 2 days remote, with flexible core hours between 9 AM and 4 PM MST to accommodate personal commitments. Our engineering culture prioritizes psychological safety above all: we run blameless post-mortems for all failed experiments or deployment issues, and dedicate every Friday afternoon to "innovation time" where team members can work on personal AI projects, test new tools, or contribute to open-source agentic AI frameworks without client deliverable pressure. We host monthly AI tech talks led by both internal team members and external GCP AI experts, and provide every engineer with an annual $2,000 learning stipend to cover certification costs, conference registrations, or paid courses.

To qualify for this role, you must have a minimum of 4 years of professional experience in cloud engineering or AI engineering, with at least 2 years of hands-on experience building and deploying production-grade agentic AI systems on Google Cloud Platform. You must have proven proficiency with at least one leading agentic AI framework (LangChain, LlamaIndex, CrewAI, or AutoGen) and core GCP AI services including Vertex AI, Cloud Run, BigQuery, and Cloud Storage. Familiarity with infrastructure as code tools (Terraform, Cloud Deployment Manager) and CI/CD tools (GitHub Actions, Cloud Build, Jenkins) is required. Experience integrating AI systems with enterprise SaaS platforms (Salesforce, SAP, ServiceNow, Zendesk) is a strong plus. You must be eligible to work in the United States without employer sponsorship at the time of hire. A Bachelor's degree in Computer Science, Engineering, or a related technical field is preferred, but equivalent professional experience will be considered.

We offer a competitive total compensation package for this role, including a base salary ranging from $145,000 to $185,000 per year, adjusted for years of relevant experience and technical expertise, plus a 15% annual performance bonus tied to client delivery metrics and team OKR completion. Full benefits include 100% employer-paid medical, dental, and vision insurance for employees, with 80% coverage for enrolled dependents, a 401(k) retirement plan with a 4% employer match, unlimited paid time off with a mandatory 15-day minimum per year, 12 paid company holidays, and 6 weeks of paid parental leave for all new parents regardless of gender or caregiving status. We also provide a one-time $3,000 home office stipend for hybrid and remote team members, and fully cover all costs for GCP AI Engineer certifications and relevant professional development courses.

This role offers clear, structured career growth opportunities within our engineering organization. We have a published career progression framework for all engineering roles, with paths to advance to Lead GCP/AI Engineer, AI Solutions Architect, or Engineering Manager within 2 to 3 years for high-performing team members. We prioritize internal promotion for all leadership roles, and provide dedicated 1:1 mentorship from senior AI architects on the team to support your professional development. High-impact team members also have the opportunity to present their agentic AI projects at national AI conferences, with the company covering all travel, registration, and accommodation costs.

Our hiring process for this role consists of 4 stages, with a total timeline of 2 weeks from application receipt to formal offer. First, a 30-minute initial screening call with our Talent Acquisition team to discuss your relevant experience and alignment with the role requirements. Second, a 60-minute technical interview with our Senior GCP/AI Engineering Lead, including a live coding exercise focused on building a simple functional agentic AI workflow on GCP. Third, a 45-minute behavioral interview with the Engineering Manager and a cross-functional team member from our client success team to assess alignment with our team values and client service standards. The final stage is a 30-minute offer call with the hiring manager to discuss compensation, benefits, and next steps. All candidates will receive a personalized response within 3 business days of each interview stage, regardless of outcome. To apply, send your resume and a brief 2-3 sentence description of your favorite agentic AI project you have built to [email protected] with the subject line "Senior GCP/AI Engineer Application - [Your Full Name]". We review applications on a rolling basis and will reach out to qualified candidates within 5 business days of submission.