Hiring GCP/AI Engineer (Agentic AI) in Phoenix

Brightsol AI is a fast-growing technology company founded in 2019, specializing in the development of custom agentic AI solutions for regulated industries including healthcare, finance, and logistics across the United States. Headquartered in Phoenix with additional offices in Austin and New York, the company has grown its revenue by 120% over the past two years, serving clients such as the Phoenix Medical Center network, regional financial institutions, and mid-sized e-commerce businesses. Our team of 85 engineers, data scientists, and client success managers operates with a culture rooted in transparency, innovation, and tangible impact, with every project designed to deliver measurable efficiency gains for our end users. We are currently expanding our cloud-native AI engineering team in Phoenix and are seeking a senior GCP/AI Engineer specialized in agentic AI to join our ranks.

In this role, you will be responsible for designing, developing, and deploying autonomous AI agent systems on Google Cloud Platform, tailored to the specific operational needs of our clients. Your work will directly support the automation of high-volume, complex tasks for healthcare providers, financial services firms, and logistics operators, reducing their manual workload by up to 60% according to our internal performance benchmarks. You will collaborate closely with cross-functional teams including Java backend developers, QA automation engineers, and technical support specialists to ensure seamless integration of AI agents into existing client workflows.

Your core responsibilities will include:
1. Designing and deploying scalable agentic AI architectures on GCP using Vertex AI, Cloud Run, BigQuery, and Pub/Sub, with a target deployment time of 4 weeks per client project from kickoff to production launch.
2. Optimizing LLM, computer vision, and natural language processing models to achieve sub-200ms inference latency on GCP infrastructure, using quantization, pruning, and distributed inference techniques.
3. Building automated CI/CD pipelines with GitHub Actions and Cloud Build for AI model deployments, reducing model release cycle times by 70% compared to our previous manual deployment processes.
4. Collaborating with Java backend and QA teams to integrate AI agents into client applications, ensuring 99.9% API compatibility and compliance with security requirements for regulated sectors including HIPAA for healthcare and PCI DSS for financial services.
5. Conducting load and robustness testing of AI agents on real client datasets to guarantee a task success rate of at least 95% in simulated production environments.
6. Monitoring and maintaining GCP infrastructure dedicated to AI projects using Cloud Monitoring and Cloud Logging, resolving production incidents in under 30 minutes during business hours in line with client SLAs.
7. Writing detailed technical documentation for all projects, including architecture diagrams, maintenance procedures, and user guides for internal teams and clients, with mandatory updates after every system iteration.
8. Training technical support teams and clients on the use of deployed AI agents, leading 2-hour training sessions per project with a participant satisfaction score of at least 4.5/5.

Our work environment is hybrid, with 3 mandatory in-office days per week at our Phoenix Tech Corridor office (located near Arizona State University) for team collaboration and sprint planning, and 2 remote days per week for focused development work. We operate with flexible working hours to accommodate personal commitments, with no presenteeism policy as long as team meetings and project deadlines are respected. Each month we host dedicated innovation days to test emerging agentic AI technologies, with an annual budget of $500 per employee for conference attendance or specialized training. Leadership shares full company performance updates, project roadmaps, and growth targets with all staff every quarter, and all process improvement suggestions are reviewed by the executive team within 2 weeks of submission.

To qualify for this senior role, you must have:
- 5+ years of professional experience in AI/ML engineering, with at least 2 years dedicated to agentic AI or autonomous system development on GCP.
- Advanced mastery of Google Cloud Platform, with a Google Cloud Professional Machine Learning Engineer or Professional Cloud Architect certification strongly preferred.
- Expert-level Python programming skills, with confirmed experience using TensorFlow, PyTorch, LangChain, LlamaIndex, and Hugging Face Transformers for LLM and agent development.
- Proven experience building CI/CD pipelines for AI projects, with mastery of GitHub Actions, Cloud Build, Docker, and Kubernetes.
- Knowledge of data security best practices for regulated industries, with prior experience implementing HIPAA or PCI DSS compliance measures preferred.
- Fluent written and verbal English communication skills to collaborate with internal US-based teams and enterprise clients.
- Strong problem-solving skills, adaptability to fast-evolving AI technologies, and a collaborative mindset.

This role offers a competitive annual salary range of $145,000 to $185,000, adjusted based on your experience and relevant certifications, plus an annual performance bonus of up to 15% of your base salary tied to team and project outcomes. Our benefits package includes 90% coverage of health insurance premiums for you and your dependents, a 401(k) retirement plan with 5% employer matching, 20 paid vacation days plus 10 sick days per year, a $2,000 annual stipend for computer equipment or professional development, and eligibility for company equity options after 2 years of tenure. We also provide a personalized professional development plan with an annual $3,000 budget per employee for certifications, advanced training, or higher education courses related to your role, plus monthly mentorship sessions with company leadership to support career growth.

After 1 to 2 years in this role, you will have the opportunity to advance to a Lead AI Engineer position managing a team of 3 to 5 engineers, or to a Client Solutions AI Specialist role focused on custom architecture design for strategic enterprise accounts.

The recruitment process takes 2 weeks total and follows 4 clear steps: we will review all applications and contact qualified candidates within 3 business days. The first step is a 30-minute phone screening with our recruiting manager to discuss your experience and motivations. The second step is a 1-hour technical interview with our AI engineering team, where you will complete a practical exercise designing an agentic AI architecture on GCP for a simulated client use case. The third step is a 45-minute interview with our CTO and engineering team lead to discuss collaboration expectations and your professional growth goals. The final step is a reference check and formal job offer, issued within 2 business days of completing all interviews. If selected, your start date will be scheduled within 2 weeks of offer acceptance.