Technical Architect

Location: Bengaluru, India; Mysuru, KA, IND, India

Department: Product

Experience: 12+ Years

Skills: NoSQL, B2B SaaS, Architecture Design, Product Development & Engineering, Continuous Integration and Deployment (CI/CD), Python, MS SQL, ETL / ELT Pipelines, AWS, DevOps Engineering

• Define and own the overall technical architecture for MADTECH.AI's cloud-native multi-tenant SaaS platform. Design scalable, secure, resilient, and highly available distributed systems capable of supporting enterprise workloads.
• Architect multi-tenant solutions with tenant isolation, role-based access control (RBAC), feature management, licensing, subscription management, and usage metering.
• Define architecture principles, engineering standards, technical guidelines, design patterns, and best practices.
• Establish architecture governance through Architecture Decision Records (ADRs), design reviews, technical standards, and governance processes.
• Drive technical decision-making and evaluate architectural trade-offs across engineering initiatives.
• Lead technology evaluations and recommend modern technologies aligned with business strategy.
• Drive platform modernization initiatives while reducing technical debt and improving long-term maintainability.

• Design and implement enterprise-grade cloud-native architectures on AWS.
• Architect cloud-native applications leveraging services such as: Amazon EKS, EC2, Lambda, API Gateway, S3, RDS, Redshift, DynamoDB, SNS/SQS, EventBridge, CloudFront, WAF, ElastiCache, Secrets, Cognito, CloudWatch, etc.,
• Build highly available, fault-tolerant, disaster recovery-ready architectures across cloud environments.
• Design scalable infrastructure supporting high availability, business continuity, disaster recovery, and automated failover.
• Optimize cloud architecture for scalability, reliability, security, operational excellence, and cost efficiency.

• Lead architecture and design of modern cloud-native microservices-based applications.
• Define API-first architecture principles and service-oriented design standards.
• Establish engineering standards for Python, React, Node.js, TypeScript, FastAPI, and modern backend technologies.
• Define reusable software architecture patterns, coding standards, and engineering governance.
• Guide engineering teams in building highly maintainable, extensible, testable, and performant applications.
• Establish secure API lifecycle management, versioning, documentation (OpenAPI), OAuth2 authentication, rate limiting, and API governance.
• Drive adoption of Domain-Driven Design (DDD), event-driven architecture, CQRS, and scalable integration patterns where appropriate.

• Architect enterprise-scale cloud-native data platforms supporting structured, semi-structured, and unstructured data.
• Design modern Data Lake, Lakehouse, and analytical data warehouse architectures.
• Architect scalable ETL/ELT pipelines, Change Data Capture (CDC), streaming pipelines, and reverse ETL solutions.
• Design robust enterprise data models optimized for analytics, AI workloads, reporting, and self-service analytics.
• Work with PostgreSQL, Amazon Redshift, Snowflake, NoSQL databases, Redis, and distributed data stores.
• Optimize large-scale analytical workloads for performance, scalability, and cost efficiency.
• Enable high-performance data access supporting AI, reporting, and advanced marketing analytics.

• Design scalable enterprise integration frameworks using REST APIs, GraphQL, event-driven architectures, messaging systems, and asynchronous processing.
• Build reusable integration services and connectors using iPaaS principles.
• Lead integrations with MarTech, AdTech, CRM, CDP, ERP, and third-party marketing platforms.
• Design secure, scalable, and resilient integration architectures.
• Establish API lifecycle management, integration governance, versioning strategies, and developer standards.
• Design reusable connector frameworks supporting rapid onboarding of new data sources and destinations.

• Partner with AI/ML teams to integrate predictive analytics, machine learning, Generative AI, and Agentic AI capabilities throughout the platform.
• Design AI-native application architectures utilizing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and intelligent workflows.
• Architect scalable AI orchestration frameworks using technologies such as LangChain, LangGraph, Model Context Protocol (MCP), and modern AI tooling where appropriate.
• Design AI governance frameworks covering model deployment, observability, security, scalability, monitoring, and responsible AI practices.
• Support deployment and lifecycle management of production AI models through scalable MLOps practices.
• Evaluate emerging AI technologies and drive adoption where they create measurable business value.

• Ensure platform architecture follows Secure-by-Design and Zero Trust principles.
• Design secure authentication, authorization, RBAC, encryption, secrets management, secure API practices, and identity management.
• Implement cloud security best practices across AWS services.
• Collaborate with Security teams to meet SOC 2, ISO 27001, GDPR, and other regulatory compliance requirements.
• Drive threat modeling, vulnerability management, secure SDLC, and security architecture reviews.
• Promote secure coding practices and proactive risk mitigation across engineering teams.

• Champion CI/CD, Infrastructure as Code (Terraform), automated testing, deployment automation, and release management.
• Collaborate with DevOps teams on Kubernetes, Docker, Helm, GitHub Actions, Jenkins, and modern deployment pipelines.
• Drive Platform Engineering initiatives to improve developer experience through self-service infrastructure, reusable platform components, and internal developer tooling.
• Improve platform observability using OpenTelemetry, distributed tracing, centralized logging, monitoring, alerting, Grafana, Prometheus, CloudWatch, and New Relic.
• Define Service Level Objectives (SLOs), Service Level Indicators (SLIs), operational metrics, and platform reliability standards.
• Lead architecture reviews, design documentation, performance optimization, capacity planning, and engineering governance.

• Provide technical leadership and mentorship to engineering teams, technical leads, and senior engineers.
• Drive architecture strategy and long-term technology roadmap aligned with business objectives.
• Guide solution design, technology selection, and architectural trade-offs.
• Partner closely with Product Management, Engineering Leadership, AI/Data Science, DevOps, Security, and Customer Success teams.
• Participate in product roadmap planning and strategic technology decisions.
• Lead architecture review boards, technical governance forums, and engineering design reviews.
• Mentor engineers on software architecture, cloud-native design, distributed systems, and engineering best practices.
• Represent engineering in executive discussions, customer technical engagements, and strategic architecture planning.
• Foster a culture of innovation, engineering excellence, collaboration, and continuous improvement.

• Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline.
• 12+ years of software engineering experience, including 5+ years in solution/application architecture.
• Proven experience architecting enterprise-scale multi-tenant B2B SaaS platforms.
• Strong experience designing cloud-native applications on AWS.
• Deep understanding of distributed systems, microservices, event-driven architecture, Domain-Driven Design (DDD), and scalable system design.
• Strong programming knowledge in Python and experience with modern frontend technologies such as React.
• Expertise in REST APIs, GraphQL, API Gateway, and integration architecture.
• Experience with SQL, PostgreSQL, Redshift, Snowflake, and NoSQL databases.
• Strong knowledge of ETL/ELT frameworks and large-scale data platforms.
• Experience working with Kubernetes, Docker, and container orchestration.
• Strong understanding of DevOps practices, CI/CD pipelines, and Infrastructure as Code.
• Experience implementing secure application architecture and cloud security best practices.
• Excellent leadership, stakeholder management, communication, and problem-solving skills.

• AWS Certified Solutions Architect (Professional) or equivalent certification.
• Experience with Marketing Analytics, MarTech, AdTech, Customer Data Platforms (CDPs), or Marketing Mix Modeling (MMM).
• Exposure to AI/ML platforms, LLMs, Generative AI, and MLOps.
• Experience with streaming technologies such as Kafka or Kinesis.
• Familiarity with observability platforms such as Grafana, Prometheus, ELK, or Datadog.
• Experience building multi-tenant SaaS platforms.
• Experience working in Agile/Scrum product organizations.

• Build a cutting-edge AI-powered Marketing Decision Intelligence platform.
• Solve large-scale data, analytics, and cloud engineering challenges.
• Work alongside experienced AI, Data Science, Product, and Engineering leaders.
• Influence product architecture and technology strategy from the ground up.
• Be part of a fast-growing, innovation-driven global SaaS organization.