Specialist - Solution Architecture

EdgeVerve Systems Limited; Nilgiris; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

EdgeVerve Systems Limited is seeking a senior technology architect to design scalable, secure architectures that align with enterprise standards in India. You will partner with stakeholders to translate requirements into robust technical solutions and act as the primary technical contact across teams.

You will provide leadership through mentoring, governance, and enforcement of best practices, coding standards, and architectural guidelines, while driving solution quality and operational

Qualifications

• Experience designing cloud-native, microservices-based architectures.
• Strong knowledge of data architecture, data formats (JSON, XML, Avro).
• Proficiency in Java, Python, or C++.
• Experience with MLOps, AI deployment, and model lifecycle.
• Security, governance, privacy, and explainability requirements.

Responsibilities

• Design and lead scalable architectures aligned with enterprise standards.
• Lead cross-functional collaboration and serve as technical point of contact.
• Mentor teams and enforce coding standards and architectural guidelines.
• Drive performance tuning, monitoring, and operational excellence.
• Define AI/ML and data integration patterns for product deployment.
• Ensure governance, auditability, lineage, and cost-effective scalability.
• Guide DataOps and MLOps across the solution lifecycle.

Skills

Cloud-native architecture

Microservices

Containerized systems

DevOps & CI/CD

Data architecture

API design

Security & governance

MLOps

Java

Python

C++

Job description

Job Description

• 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.

Requirements: