Sr Specialist - Solution Architecture

Responsibilities

1. 1. Design and lead scalable, secure, and reliable technology architectures that align with business goals and enterprise standards.
   2. Partner with stakeholders, business leaders, and cross-functional teams to gather requirements, translate them into technical solutions, and serve as the primary technical point of contact.
   3. Provide technical leadership through mentoring, governance, and the enforcement of best practices, coding standards, and architectural guidelines.
   4. Drive solution quality and operational excellence through testing, performance tuning, monitoring, troubleshooting, and collaboration with support teams to minimize downtime.
   5. Define AI/ML and data architecture patterns for product integration, including model integration, inference workflows, data pipelines, API-led integration, event-driven components, and interoperability with enterprise platforms.
   6. Ensure AI and data solutions meet enterprise requirements for governance, privacy, security, explainability, traceability, lineage, auditability, resiliency, disaster recovery, and cost-effective scalability.
   7. Guide the adoption of DataOps and MLOps practices across the solution lifecycle, including deployment, monitoring, retraining, validation, versioning, release governance, and ongoing optimization.
      Skill requirements
      1. 1. Strong expertise in technical architecture design for cloud-native, microservices-based, and containerized enterprise systems.
         2. Hands-on knowledge of performance optimization, technical design reviews, DevOps, and CI/CD practices for scalable and reliable solution delivery.
         3. Solid understanding of database design and management, data formats such as JSON, XML, and Avro, and enterprise data architecture including modeling, integration, quality, metadata, and lineage.
         4. Experience with analytics and data platforms, query optimization, and designing streaming, batch, and enterprise-scale data pipelines.
         5. Good knowledge of security, compliance, data governance, privacy, explainability, and audit requirements for enterprise technology and AI-enabled solutions.
         6. Proficiency in programming languages such as Java, Python, or C++, along with knowledge of process automation and modern integration patterns including APIs and event flows.
         7. Knowledge of machine learning, deep learning frameworks such as TensorFlow and PyTorch, Large Language Models, RAG, vector stores, prompt orchestration, and responsible AI controls.
         8. Experience with MLOps and AI operationalization, including feature engineering workflows, model lifecycle management, deployment, evaluation, drift monitoring, retraining, and production support.
            

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