Senior Solution Architect
Salary undisclosed
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Responsibilities:
- Architect and Design Solutions: Lead the design and architecture of scalable, secure, and resilient cloud solutions across multiple cloud platforms (AWS, Azure, Google Cloud).
- Data and AI Integration: Develop and implement data strategies, including data lakes, data warehouses, and real-time data processing. Integrate AI and machine learning models to enhance business processes and decision-making.
- Technical Leadership: Provide technical leadership and guidance to development teams, ensuring best practices in cloud architecture, data management, and AI implementation.
- Stakeholder Collaboration: Collaborate with business stakeholders to understand requirements and translate them into technical solutions. Communicate complex technical concepts to non-technical stakeholders.
- Innovation and Strategy: Stay updated with the latest trends and technologies in cloud computing, data, and AI. Drive innovation and strategic initiatives to enhance our technology stack.
- Security and Compliance: Ensure that all solutions adhere to security best practices and compliance requirements. Implement robust security measures to protect data and applications.
- Performance Optimization: Monitor and optimize the performance of cloud-based applications and data systems. Identify and resolve performance bottlenecks.
- Documentation and Training: Create comprehensive documentation for designed solutions. Provide training and support to internal teams and clients.
Qualifications:
- Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.
- Experience: Minimum of 8 years of experience in solution architecture, with a focus on cloud computing, data management, and AI.
- Cloud Expertise: Proficiency in multiple cloud platforms (AWS, Azure, Google Cloud). Certification in one or more cloud platforms is a plus.
- Data Management: Strong experience with data architecture, data lakes, data warehouses, and real-time data processing.
- AI and Machine Learning: Hands-on experience with AI and machine learning frameworks and tools (e.g., TensorFlow, PyTorch, Azure ML, AWS SageMaker).
- Technical Skills: Proficiency in programming languages such as Python, Java, or C#. Experience with containerization (Docker, Kubernetes) and CI/CD pipelines.
- Soft Skills: Excellent communication, problem-solving, and leadership skills. Ability to work collaboratively in a fast-paced environment.
- Certifications: Relevant certifications in cloud computing, data management, and AI are highly desirable.
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