Master Service Solution Architect

Oracle

6d

About The Position

The role requires a deep understanding of AI services, industry hyperscalers including infrastructure, data management, and distributed applications, as well as a proficiency in tools used to manage services in cloud environments. The ideal candidate will be comfortable architecting and deploying cloud-native and hybrid applications using native services and components on major hyperscaler platforms.
Mission
This person will play a crucial role in developing service innovation, optimizing processes, and delivering insights that empower the organization to make informed, data-driven decisions. They are passionate about cloud enterprise architecture and is collaborative and driven to apply their technical skills to real-world applications. This role will contribute to portfolio management and business development of Oracle Customer Success Services offerings for our customers that feature the latest Oracle technologies and capabilities. The architect will ensure that all deployments adhere to security, compliance, and cost optimization best practices for each hyperscaler.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Engineering or related field required; Ph.D. preferred
• Multiple cloud certifications such as Oracle Certifications: OCI Associate Architect, OCI Architect Professional; AWS Certifications: AWS Solutions Architect, AWS Security Specialist, Microsoft Certifications: Azure Security Engineer, Associate, Azure Solutions Architect, Google Certifications: GCP Cloud Architect Certification
• Certification as TOGAF and/or Zachman Enterprise Architect preferred
• 15+ years in IT services, including significant direct experience in customer facing and service management
• Proficient with cloud platforms (OCI, GCP, Azure, AWS) and environments on-Premise for Enterprise application operations
• Hands-on experience architecting, deploying, and managing enterprise workloads and data migrations on at least two major hyperscaler platforms (AWS, Azure, GCP).
• Familiarity with natural language processing (NLP), computer vision, and deep learning
• Prior experience deploying and maintaining AI models in production environments
• Commercial constructs and contract management, including construction of TCO analyses and pricing for complex service offers
• Large scale IT project and program management, including major transformation, migration, and solution deployment projects
• Strong analytical and problem-solving abilities to translate business problems into technical requirements
• Excellent written and verbal communication capability to convey complex concepts to both technical and non-technical audiences
• Automation Tools: Deep expertise with infrastructure-as-code and automation for hyperscalers (CloudFormation for AWS, ARM/Bicep for Azure, Deployment Manager for GCP, and cloud-agnostic tools like Ansible or Terraform)
• Monitoring: Familiarity with hyperscaler-native monitoring/logging tools: OCI OEM and O&M, AWS CloudWatch, Azure Monitor, GCP Operations Suite and cloud agnostic tools like Prometheus, Grafana or Kibana
• Networking Elements: subnets, route tables, gateways, DNS, load balancers, firewalls, VPNs
• Cloud Networking: VPCs, VCNs, WAN connectivity, Fastconnect (OCI), ExpressRoute (Azure), Direct Connect (AWS), Cloud Interconnect (GCP)
• Hybrid Security: OCI Vault (Oracle), Key Vault (Azure), KMS (AWS/GCP), and cross-cloud identity (OCI IAM, Azure AD, AWS IAM, Google Workspaces)
• Resilience: Ability to analyze and optimize cloud resource allocation, selection of instance types, and storage tiers to balance performance, HA, Business Continuity, and cost in hyperscaler environments
• Structured Data: Oracle Database, NoSQL, MySQL, and other databases
• Data Processing: Thorough grasp of Spark, Kafka, Hadoop, MapReduce
• Data Design: Proficient in handling large datasets, data structures and algorithms for machine learning workflows
• Analytics: Comprehensive predictive, diagnostic and prescriptive analytics skills and usage of relevant tools
• Development Tools: Language fluency in Python, R, SQL, Java, Langchain
• AI/ML Frameworks: TensorFlow, PyTorch, and GenAI Frameworks like Autogen
• Data Science: MLOps practices requiring projects, model deployments, data science SDKs, Jupyterlab notebooks
• AI Governance: Understanding of ethical AI and responsible AI practices
• DevOps: tooling such as Jenkins, Gitlab, and testing tools such as Selenium, Loadrunner and RUEI

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees