About the Role
We are looking for an experienced Software Architect with deep expertise in designing and architecting large-scale enterprise applications, with strong hands-on experience in AI/ML technologies, AWS Cloud, and modern DevOps practices. The ideal candidate should have strong proficiency in at least one backend technology stack — Java or Python — along with solid experience in cloud-native architectures, distributed systems, AI/ML-driven solutions, LLM/GenAI integrations, and AWS services.
This is a high-impact, strategic role involving technical leadership, solution design, and architectural decision-making across complex enterprise environments, with an emphasis on integrating AI and cloud capabilities into modern applications.
Key Responsibilities
Enterprise Architecture
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Lead end-to-end architecture design for enterprise-grade applications using Java or Python.
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Define system architecture, integration patterns, microservices, and cloud-native solutions.
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Design API frameworks, asynchronous systems, and scalable distributed architectures.
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Conduct architecture reviews, performance tuning, and security assessments.
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Create and maintain High Level Design (HLD) and Low-Level Design (LLD) documentation.
AWS Cloud & DevOps
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Architect and implement solutions leveraging AWS services: EC2, Lambda, S3, RDS, DynamoDB, SageMaker, ECR, CloudWatch, IAM, and KMS.
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Design and manage serverless architectures using AWS Lambda and the Serverless Framework.
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Build and manage containerized workloads using Docker and Kubernetes (EKS).
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Implement Infrastructure-as-Code using Terraform (HCL), including remote state management and OIDC best practices.
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Establish and optimize CI/CD pipelines for continuous delivery across environments.
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Ensure secure, compliant, and cost-optimized use of AWS cloud resources.
AI & GenAI Architecture
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Design scalable AI/ML system architectures and integrate them into enterprise applications.
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Lead integration of LLM-based solutions using LangChain, OpenAI APIs, and related GenAI frameworks.
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Own the conversion of data science notebooks into production-ready, maintainable code and pipelines.
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Collaborate with data scientists to operationalize machine learning models in production environments.
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Define data pipelines, governance, and storage solutions for AI workloads.
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Identify opportunities to leverage AI for predictive analytics, intelligent automation, and decision support.
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Ensure fairness, transparency, and compliance in AI adoption (Responsible AI).
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Establish KPIs and monitoring frameworks for AI systems to ensure accuracy and scalability.
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Assess risks related to AI deployment including bias, adversarial attacks, and data privacy.
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Stay ahead of advancements in generative AI, LLMs, and applied machine learning.
Security & Compliance
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Implement identity and access management using Azure AD SAML and AWS IAM.
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Enforce secret management and encryption best practices using AWS KMS and secrets management tools.
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Ensure GDPR-aware handling of PII across data pipelines and application layers.
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Assess security risks related to cloud deployments, AI systems, and third-party integrations.
Technical Leadership & Collaboration
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Provide technical leadership and guide development teams across multiple projects.
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Evaluate and recommend technology stacks, frameworks, and architectural approaches.
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Collaborate with Product, QA, DevOps, Security, and UI/UX teams for seamless delivery.
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Promote design patterns, code quality, CI/CD adoption, and cloud best practices.
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Mentor engineering teams on AI concepts, tools, frameworks, and architectural thinking.
Required Technical Skills
Core Backend (any one stack):
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Java — Spring Boot, Spring, Hibernate/JPA
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Python — Django, Flask, FastAPI; including familiarity with converting notebooks to production code
AI & GenAI:
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LangChain, OpenAI API integration
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LLM application design, prompt engineering, RAG pipelines
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ML model operationalization and production deployment
Architecture & Design:
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Microservices, distributed systems, event-driven architectures
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RESTful API design and integration patterns
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Messaging systems — Kafka, RabbitMQ, Service Bus
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OOP, SOLID principles, DDD, clean architecture
AWS Cloud & Infrastructure:
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EC2, Lambda, S3, RDS, DynamoDB, SageMaker, ECR, CloudWatch, IAM, KMS
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Serverless Framework, AWS Lambda-based architectures
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Terraform (HCL) — remote state, OIDC best practices, modules
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Docker, Kubernetes (EKS)
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CI/CD pipeline design and implementation
Security & Compliance:
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Azure AD SAML / SSO integration
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AWS KMS and secrets management
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GDPR-aware PII handling in data and application layers
Databases:
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SQL — PostgreSQL, MySQL, SQL Server, Oracle
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NoSQL — MongoDB, Cassandra, Redis, DynamoDB
Good to Have (Future Scope):
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React / Next.js frontend development exposure
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Workday API / ATS integration experience
Soft Skills & Leadership:
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Strong architectural decision-making and communication skills
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Clear documentation capability (HLD/LLD)
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Ability to lead and influence engineering teams and stakeholders
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Strategic mindset focused on scalability, reliability, and performance
Minimum Education
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Bachelor's degree in Computer Science / Engineering or equivalent
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MBA from IIM preferred for candidates applying under the 3–5 year relevant experience criteria
