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1. Microsoft Solution Architect
- Bachelor’s or master’s degree in computer science, Information Technology, or related field.
- Proven experience as an Application Architect or Solution Architect with expertise in Microsoft technologies (.NET, C#, ASP.NET, SQL Server, Azure).
- In-depth knowledge of microservices architecture, design, distributed systems, and API-driven development.,
- Understand of Biztalk and Other middleware components
- Hands-on experience with containerization and orchestration technologies such as Docker, Kubernetes.
- Strong understanding of cloud computing principles, Azure/AWS/GCP services, and PaaS/SaaS offerings for building scalable, resilient applications.
- Proficiency in security concepts, identity management (Azure Active Directory), data protection, and compliance frameworks.
- Excellent communication, leadership, and problem-solving skills with the ability to collaborate effectively across teams and influence technical decisions.
2. Solution Architect
- Bachelor’s or Master’s degree in Computer Science, Information Technology, or related field.
- Proven experience as an Application Architect or Solution Architect with expertise in Microsoft technologies (.NET/ C#/ASP.NET,/Java/Spring Boot/ Spring Cloud, Azure).
- In-depth knowledge of microservices architecture, design patterns (e.g., CQRS, Saga), distributed systems, and API-driven development.
- Hands-on experience with containerization and orchestration technologies such as Docker, Kubernetes.
- Strong understanding of cloud computing principles, Azure/AWS/GCP services, and PaaS/SaaS offerings for building scalable, resilient applications.
- Proficiency in security concepts, identity management (Azure Active Directory), data protection, and compliance frameworks.
- Experience with DevOps practices, CI/CD pipelines, version control systems (Git), automated testing, and infrastructure automation tools (e.g., Terraform, Ansible,Helm).
- Excellent communication, leadership, and problem-solving skills with the ability to collaborate effectively across teams and influence technical decisions.
3. AI expert
- Master’s or Ph.D. degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
- Proven experience as an AI Engineer, Data Scientist, or AI Researcher with hands-on experience in AI model development and deployment.
- Strong programming skills in languages such as Python, R, Java, or C++, with proficiency in AI frameworks (TensorFlow, PyTorch, scikit-learn) and libraries for data manipulation and analysis.
- In-depth knowledge of machine learning algorithms (supervised, unsupervised, reinforcement learning), deep learning architectures (CNNs, RNNs, GANs), and NLP techniques.
- Experience with cloud platforms (AWS, Azure, GCP), big data technologies (Hadoop, Spark), and distributed computing frameworks for AI model training and deployment.
- Familiarity with software development methodologies (Agile, Scrum), version control systems (Git), and DevOps practices for AI model lifecycle management.
- Strong analytical and problem-solving skills, with the ability to translate business requirements into AI solutions and actionable insights.
- Excellent communication, collaboration, and presentation skills, with the ability to explain complex AI concepts to non-technical stakeholders.
4.Integration and ISO 20022 Implementation Expert
- Bachelor’s or Master’s degree in Computer Science, Information Technology, or related field.
- Proven experience as an Integration Architect, Integration Engineer, or similar role with expertise in integration technologies, standards, and protocols.
- Strong understanding of ISO 20022 standards, financial messaging formats (e.g., SWIFT MT, FIX), and XML-based message formats for financial transactions.
- Experience with integration middleware platforms (e.g., Service Bus, API Gateway, SFTP, IDM), API management tools, and message brokering technologies.
- Knowledge of RESTful and SOAP APIs, API design principles, OAuth, and API security standards for secure and scalable integration solutions.
- Familiarity with data governance frameworks, data modeling, data mapping tools, and ETL (Extract, Transform, Load) processes for data integration and transformation.
- Experience with cloud integration, microservices architecture, and containerization technologies (Docker, Kubernetes) is a plus.
- Strong analytical, problem-solving, and communication skills, with the ability to collaborate effectively with cross-functional teams, stakeholders, and external partners.
- Understanding of financial services, banking systems, payment processing, and regulatory requirements related to financial messaging and data privacy.