EdgeVerve Systems→
Senior Specialist - Solution Architecture at EdgeVerve… · Location…
ExperiencedOn-siteNot specified
Job Description
JOB Description:
Responsibilities
Skill requirements
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.