About this role
Senior Specialist - Solution Architecture
Job Description: JOB Description: Responsibilities • • Design and lead scalable, secure, and reliable technology architectures that align with business goals and enterprise standards. • 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. • Provide technical leadership through mentoring, governance, and the enforcement of best practices, coding standards, and architectural guidelines. • Drive solution quality and operational excellence through testing, performance tuning, monitoring, troubleshooting, and collaboration with support teams to minimize downtime. • 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. • Ensure AI and data solutions meet enterprise requirements for governance, privacy, security, explainability, traceability, lineage, auditability, resiliency, disaster recovery, and cost-effective scalability. • 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 • • Strong expertise in technical architecture design for cloud-native, microservices-based, and containerized enterprise systems. • Hands-on knowledge of performance optimization, technical design reviews, DevOps, and CI/CD practices for scalable and reliable solution delivery. • 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. • Experience with analytics and data platforms, query optimization, and designing streaming, batch, and enterprise-scale data pipelines. • Good knowledge of security, compliance, data governance, privacy, explainability, and audit requirements for enterprise technology and AI-enabled solutions. • 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. • Knowledge of machine learning, deep learning frameworks such as TensorFlow and PyTorch, Large Language Models, RAG, vector stores, prompt orchestration, and responsible AI controls. • Experience with MLOps and AI operationalization, including feature engineering workflows, model lifecycle management, deployment, evaluation, drift monitoring, retraining, and production support.