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