Hiring GCP/AI Engineer (Agentic AI) in Phoenix

BrightSol is a US-based artificial intelligence solutions provider founded in 2019, specializing in building custom production-grade agentic AI systems for enterprise clients across healthcare, financial services, logistics and retail. With 120+ employees across offices in Phoenix, Austin and fully remote teams spanning 18 US states, we help mid-market and enterprise clients reduce operational costs by 30-40% on average by automating complex, repetitive workflows with tailored AI agent solutions that integrate seamlessly with existing cloud and on-premise infrastructure. Our engineering team is made up of 25 senior AI engineers, backend developers and MLOps specialists who prioritize delivering measurable business impact over internal process overhead, with a culture built on autonomy, continuous learning and work-life balance.

We are hiring a Senior GCP/AI Engineer specializing in Agentic AI to join our Phoenix-based engineering team, with flexible hybrid and fully remote options available for qualified candidates based anywhere in the United States. This is not a research or prototyping role: you will be responsible for building, deploying and scaling production agentic AI systems that run for clients 24/7, with a focus on reliability, security and compliance with industry-specific regulations. You will work closely with cross-functional teams of data scientists, backend engineers, client success managers and security specialists to deliver custom AI solutions that meet strict contractual SLAs and deliver tangible ROI for our clients.

Your core responsibilities will include:
1. Designing, developing and deploying end-to-end agentic AI solutions on Google Cloud Platform (GCP), leveraging Vertex AI, Cloud Run, BigQuery and Google Kubernetes Engine (GKE), ensuring 99.9% uptime for production client deployments and response times under 200ms for high-priority agent workflows.
2. Fine-tuning and optimizing large language models (LLMs) including Google Gemini, Llama 3 and Mistral for domain-specific client use cases, reducing hallucination rates by at least 25% for regulated industry clients (healthcare, finance) through custom prompt engineering, retrieval-augmented generation (RAG) implementation and supervised fine-tuning pipelines.
3. Building and maintaining scalable MLOps pipelines for agentic AI systems, automating model retraining, validation and deployment processes to reduce release cycles from 2 weeks to 3 business days, using tools including Vertex AI Pipelines, Docker, Kubernetes and GitHub Actions.
4. Integrating agentic AI tools with existing client enterprise systems including CRM platforms (Salesforce), ERP tools (SAP S/4HANA) and internal data warehouses, ensuring seamless data flow and compliance with GDPR, HIPAA and CCPA data privacy regulations for all client deployments.
5. Collaborating with senior data scientists to translate client business requirements into technical AI specifications, leading technical design reviews for 4-6 agentic AI projects per quarter and providing clear, detailed documentation for client engineering teams to support post-deployment maintenance and troubleshooting.
6. Troubleshooting and resolving production incidents for deployed agentic AI systems, responding to critical client issues within 1 hour during business hours and delivering root cause analysis reports within 4 hours of incident resolution to meet contractual SLA requirements.
7. Contributing to internal AI engineering best practices, leading monthly knowledge sharing sessions for the 25-person engineering team and mentoring 2-3 junior AI engineers on GCP best practices, agentic AI architecture patterns and LLM optimization techniques.
8. Conducting performance testing and load testing for agentic AI deployments, ensuring systems can handle 10,000+ concurrent user requests without degradation in response quality, and optimizing cloud infrastructure costs to keep monthly GCP spend per client deployment 15% below budget targets.

Our work environment is built to support both collaboration and deep focused work. We operate a flexible hybrid model, with optional in-person collaboration days at our modern Phoenix office for local team members, and full remote eligibility for all engineering roles based anywhere in the US. We have a no-meeting policy every Wednesday to allow for uninterrupted development time, and allocate 10% of every engineer’s work time to R&D projects exploring new agentic AI use cases and emerging AI tools. Our team prioritizes work-life balance, with unlimited PTO that includes a mandatory 15-day minimum per year, no after-hours messaging expectations outside of scheduled on-call rotations, and fully paid parental leave of 12 weeks for all new parents. We host bi-annual team offsites in locations across the US for in-person collaboration, professional development and team building.

To qualify for this role, you must have:
- 5+ years of professional experience in software engineering, with at least 3 years of specialized experience building and deploying AI/ML systems on cloud platforms, preferably GCP
- A proven track record of delivering at least 3 production-grade agentic AI systems to enterprise clients, with documented evidence of measurable business impact (e.g. 30% reduction in manual workflow time, 25% reduction in operational costs for client use cases)
- Expert-level proficiency in Python, with hands-on experience using popular AI/ML frameworks including TensorFlow, PyTorch, LangChain and LlamaIndex for building agentic AI workflows
- Deep understanding of GCP AI/ML services including Vertex AI, BigQuery, Cloud Run, GKE and Cloud IAM, with a GCP Professional Machine Learning Engineer or AI Engineer certification preferred
- Strong experience with MLOps tools and practices, including CI/CD pipeline development, model monitoring, automated retraining workflows and infrastructure-as-code using Terraform or GCP Deployment Manager
- Excellent communication skills, with the ability to explain complex technical AI concepts to non-technical client stakeholders and lead technical design discussions with cross-functional teams
- Experience working with regulated industry clients (healthcare, finance) and implementing data privacy and security controls for AI systems is a strong plus

You will be required to work with the following technical tools and platforms as part of this role: GCP (Vertex AI, BigQuery, Cloud Run, GKE, Cloud IAM), Python, TensorFlow, PyTorch, LangChain, LlamaIndex, Docker, Kubernetes, Terraform, GitHub Actions, Salesforce, SAP S/4HANA, Jira, Git, REST APIs, LLM fine-tuning tooling, RAG implementation frameworks and MLOps monitoring platforms.

We offer a highly competitive total compensation package aligned with US tech industry standards for senior AI engineering roles. The base salary for this position ranges from $165,000 to $210,000 per year, plus an annual performance bonus of 10-20% of base salary and equity grants for all full-time senior employees. Our benefits package includes 100% employer-paid health, dental and vision insurance for you and your dependents, a $2,000 annual professional development stipend for conferences, courses and certifications, a $1,500 home office stipend for remote employees, and access to a 401(k) plan with 4% employer match. We also offer paid parental leave of 12 weeks for all new parents, flexible scheduling to accommodate caregiving responsibilities or personal commitments, and access to exclusive industry AI events and networking opportunities.

This role offers clear pathways for career growth in the fast-expanding agentic AI space. You will have direct access to senior leadership to propose new product ideas and lead cross-team AI initiatives, with promotion pathways to Staff AI Engineer, Principal AI Engineer or AI Engineering Manager roles based on performance and business impact. We cover 100% of costs for relevant professional certifications including GCP AI Engineer and Machine Learning Engineer certifications, and provide regular opportunities to attend leading AI conferences including Google I/O, NeurIPS and the Enterprise AI Summit.

Our hiring process for this role is transparent and efficient, with a total timeline of 2 weeks from application to offer. The steps are as follows: 1. Initial 30-minute screening call with our talent acquisition partner to discuss your experience and the role requirements. 2. 60-minute technical interview with the Senior AI Engineering Manager to review your past agentic AI projects and assess your technical fit for the role. 3. 90-minute live technical exercise where you will design a simple agentic AI workflow for a sample client use case, followed by a 30-minute Q&A with the engineering team. 4. Final 30-minute call with the VP of Engineering to discuss team culture, career goals and any questions you have about the role. All candidates will receive feedback 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 most impactful agentic AI project to [email protected] with the subject line "Application for GCP/AI Engineer (Agentic AI) - [Your Full Name]". We review applications on a rolling basis and will reach out to qualified candidates within 5 business days of submission.