What does a successful Senior Cloud Solutions Architect do at Fiserv?
You will be responsible for designing secure, compliant, and scalable AI/ML solutions, influencing product direction, and enabling cloud innovation across the enterprise. You will collaborate with leadership on technical strategy, governance, operational, and enablement programs. This role requires strong leadership skills, forward thinking, and the ability to work effectively on a diverse team.
What you will do:
- Architect secure, scalable end-to-end AI/ML solutions on AWS, GCP, and Azure.
- Serve as a technical leader and subject matter expert in cloud-native AI/ML services (e.g., Vertex AI, SageMaker, Bedrock, Anthropic, OpenAI integrations).
- Embed security and compliance controls into AI/ML architectures, including model governance, access controls, and data protection.
- Translate concepts into actionable insights for business and technical stakeholders.
- Guide customers and internal teams in selecting and deploying secure, compliant GenAI solutions at scale.
- Lead enablement workshops, solution design sessions, and foster technical communities.
- Implement MLOps best practices, including CI/CD pipelines and observability.
- Maintain awareness of cloud and AI-related security risks, including prompt injection, adversarial threats, threat modeling, and compliance standards (e.g., GDPR, PCI).
What you will need to have:
- 8+ years of experience in cloud architecture, with 4+ years with AI/ML solution design and implementation.
- Deep hands-on expertise with AWS, GCP, an/or Azure, services, and tooling.
- Strong experience with modern ML frameworks (TensorFlow, PyTorch, Hugging Face, etc.) and MLOps tools (Kubeflow, MLflow, Vertex AI Pipelines).
- Proven record designing and deploying secure, enterprise-grade cloud applications.
- Solid understanding of cloud security, data privacy, and compliance standards.
- Exceptional communication skills; able to influence and educate technical and non-technical audiences alike.
- Demonstrated experience leading cross-functional teams and mentoring.
- Familiarity with AI/ML-related security techniques such as model auditing, explainability, LLM endpoint protection, and responsible AI frameworks.
What would be great to have:
- Experience working with enterprise-scale financial services or other regulated industries.
- Background in software engineering, DevSecOps, or AI security research.
- Certifications: AWS Certified Machine Learning – Specialty, Google Cloud Professional ML Engineer, or security-focused credentials (e.g., CISSP, AWS Security Specialty).
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