Hiring GCP/AI Engineer (Agentic AI) in Phoenix

Company Context:
BrightSol is a fast-growing U.S.-based technology firm specializing in the design and deployment of custom AI solutions and cloud infrastructure for clients in the healthcare, financial services, and logistics sectors. With offices across the country and a team of 80+ engineers and data scientists, we help organizations implement autonomous AI systems and scalable cloud architectures to drive operational efficiency and innovation. Our Phoenix office, located in the Tempe district near Arizona State University, is expanding to meet rising demand for GCP-based AI solutions, and we are hiring a Senior GCP/AI Engineer (Agentic AI) to join our growing engineering team.

Role Introduction:
As a Senior GCP/AI Engineer focused on Agentic AI, you will be responsible for designing, deploying, and maintaining autonomous AI systems on Google Cloud Platform, as well as building and optimizing data pipelines and machine learning models tailored to our clients’ needs. You will work closely with cross-functional teams including data scientists, backend developers, and client success managers to ensure the reliability, performance, and compliance of all deployed solutions. This is a hands-on technical role for an experienced engineer who is passionate about cutting-edge AI technologies and cloud architecture.

Key Responsibilities:
1. Design and deploy scalable agentic AI architectures on GCP using Vertex AI, BigQuery, Cloud Functions, and Google Kubernetes Engine (GKE), guaranteeing 99.95% uptime for critical client systems.
2. Optimize GCP infrastructure costs for AI projects by implementing automatic scaling strategies and resource management practices, reducing cloud spending by 15-20% per project without compromising performance.
3. Develop and maintain ETL/ELT data pipelines for machine learning model training and deployment, using Apache Beam, Dataflow, and Cloud Storage, with a data processing time of under 2 hours for 1TB datasets.
4. Collaborate with data scientists to convert machine learning models into production-ready services on GCP, using MLOps tools including Vertex AI Pipelines, Kubeflow, and MLflow, cutting model deployment time by 30% compared to existing processes.
5. Implement monitoring and alerting systems for AI infrastructure and deployed models using Cloud Monitoring, Prometheus, and Grafana, detecting and resolving incidents in under 15 minutes in line with client SLAs.
6. Conduct security and compliance audits for GCP infrastructures hosting sensitive data (healthcare, financial), adhering to HIPAA, GDPR, and PCI DSS regulations, ensuring 100% compliance for all client projects.
7. Train internal teams (developers, support staff) on GCP and agentic AI best practices by leading monthly workshops and writing detailed technical documentation, improving team deliverable quality by 25%.
8. Participate in client scoping meetings to assess AI and cloud infrastructure needs, and propose tailored solutions aligned with client budget and performance constraints, achieving a 95% client satisfaction rate for delivered projects.

Work Environment & Culture:
We operate a hybrid work model, with 3 days per week at our Phoenix office and 2 days of remote work to support work-life balance. Our engineering team is composed of 12 specialists in cloud and AI, with diverse profiles (GCP engineers, data scientists, backend developers) from 7 different countries, fostering a collaborative and inclusive environment. We prioritize knowledge sharing and innovation: every engineer receives 10 days of paid training per year to stay up to date with the latest GCP and AI technologies, and we host quarterly hackathons to develop high-potential internal projects. The office culture is relaxed, with no formal dress code, monthly team afterworks, and regular team-building events. We value autonomy: every engineer can propose and lead innovative pet projects that align with our business goals, with dedicated budget allocated to high-potential initiatives.

Candidate Requirements:
- 5+ years of professional experience in a cloud engineering or AI engineering role, with at least 3 years of specific hands-on experience on Google Cloud Platform (GCP).
- Proven track record of designing and deploying agentic AI systems, machine learning models, and data pipelines on GCP for enterprise clients.
- Mastery of core GCP services: Vertex AI, BigQuery, Cloud Functions, Google Kubernetes Engine (GKE), Cloud Storage, and Cloud Monitoring.
- Experience with MLOps tools: Vertex AI Pipelines, Kubeflow, MLflow, TensorFlow, and PyTorch.
- Proficiency in Python (required); knowledge of Java or Go is a plus.
- Experience with containerization tools: Docker and Kubernetes.
- Familiarity with security and compliance regulations for sensitive data (HIPAA, GDPR, PCI DSS) is a plus.
- Strong teamwork skills, ability to communicate clearly with both technical and non-technical stakeholders, and capacity to solve complex problems autonomously.
- Google Cloud Professional Cloud Architect or Google Cloud Professional Machine Learning Engineer certification is a plus.
- Bachelor’s degree in Computer Science, Software Engineering, or a related field is preferred.

Technical Skills (Verified Tools):
- Cloud Platforms: Google Cloud Platform (GCP), Vertex AI, BigQuery, Cloud Functions, GKE, Cloud Storage, Cloud Monitoring
- MLOps & AI Tools: Vertex AI Pipelines, Kubeflow, MLflow, TensorFlow, PyTorch, Apache Beam, Dataflow
- Programming Languages: Python, Java, Go
- Containerization & Orchestration: Docker, Kubernetes
- Monitoring & Observability: Prometheus, Grafana
- Compliance Frameworks: HIPAA, GDPR, PCI DSS

Benefits & Compensation:
We offer a highly competitive compensation package to reflect your senior expertise:
- Base salary: $145,000 - $185,000 per year, adjusted based on years of experience and technical skills.
- Annual performance bonus: up to 15% of your base salary, tied to individual and team performance metrics.
- Full health coverage (medical, dental, vision) with 90% of premiums covered for you and your dependents.
- 20 days of paid time off (PTO) per year, plus 10 company-paid public holidays.
- 401(k) retirement plan with 6% employer match, available after 6 months of tenure.
- Eligibility for company stock options after 1 year of employment, for all full-time senior roles.
- Annual learning and development budget of $3,000 per employee to cover certifications, conference attendance, or professional training courses.
- Hybrid work model with 2 remote days per week, and flexible working hours (core hours 10am-3pm, with the option to adjust your schedule between 7am and 7pm to accommodate personal needs).
- Fully equipped workstation: high-performance laptop, dual monitors, noise-canceling headset, and full access to required GCP and AI development tools.

Career Growth Opportunities:
This role is designed for long-term career growth within BrightSol. After 1-2 years of strong performance, you will be eligible for promotion to Lead GCP/AI Engineer, managing a team of 3-5 junior and mid-level engineers, or to AI Solutions Architect, responsible for designing end-to-end AI and cloud solutions for our most strategic enterprise clients. We also offer internal mobility opportunities to our other U.S. offices (San Francisco, New York, Austin) for employees looking to relocate. You will have a dedicated career development plan, with bi-annual performance reviews with your manager to identify growth goals, training needs, and promotion pathways tailored to your professional aspirations.

Recruitment Process & Next Steps:
If your profile matches the requirements for this role, our talent acquisition team will contact you within 5 business days to schedule a 30-minute initial phone screen with the head of our engineering team. This will be followed by a 2-hour remote technical assessment, where you will be asked to design a GCP architecture for a real-world agentic AI use case. If you pass the technical assessment, you will be invited to a 1-hour virtual or in-person (for Phoenix-based candidates) technical interview with the engineering team, followed by a 30-minute final interview with our Head of Operations. The full recruitment process takes between 2 and 3 weeks from initial contact to job offer. To apply, send your resume and a link to your GitHub profile or AI project portfolio to [email protected], with the subject line "Application: Senior GCP/AI Engineer Phoenix - [Your Full Name]". We review all applications and will respond to every candidate, even if your profile does not fully match the role requirements.