Description

Responsibilities:

• Own end-to-end system design for new and existing applications, translating functional requirements into detailed architecture (HLD/LLD), including data flow, integration patterns, and API contracts.
• Define and enforce non-functional requirements (NFRs) — performance, scalability, availability, security, maintainability, observability — and validate designs against them through architecture reviews, load testing strategy, and capacity planning.
• Architect solutions using the Java stack (Core Java, Spring Boot/Spring Cloud, microservices, REST/gRPC APIs, messaging systems like Kafka/RabbitMQ).
• Evaluate and integrate AI/ML capabilities into applications — e.g., LLM-based features, RAG pipelines, ML model serving, prompt engineering, or integration with AI platforms (OpenAI, Anthropic, AWS Bedrock, SageMaker, etc.), depending on project needs.
• Design and guide deployment architecture on AWS — compute (EC2, ECS/EKS, Lambda), storage (S3, RDS, DynamoDB), networking (VPC, API Gateway), and security (IAM, KMS).
• Conduct technical risk assessments, identify architectural trade-offs, and present recommendations to stakeholders and leadership.
• Collaborate with product managers, engineering leads, and QA to ensure architecture aligns with business goals and delivery timelines.
• Create and maintain architecture documentation — system diagrams, data models, sequence diagrams, decision records (ADRs).
• Provide technical mentorship to development teams; review code/design for adherence to architectural standards.
• Drive proof-of-concepts (POCs) for new technologies, frameworks, or AI integrations before full-scale adoption.
• Participate in capacity planning, disaster recovery, and cost optimization exercises for cloud-hosted systems.

Requirements:

• Strong experience in system/solution design — able to independently produce HLD/LLD covering both functional and non-functional aspects.
• Deep hands-on expertise in the Java ecosystem: Java (8/11/17+), Spring Boot, Spring Cloud, Hibernate/JPA, RESTful services, microservices architecture.
• Practical exposure to AI/ML stack — could include LLM integration, vector databases, RAG architectures, ML frameworks (TensorFlow/PyTorch), or AI-assisted development tooling. (Depth expected varies by role — clarify with client whether this is "AI-aware architect" vs "hands-on ML engineer.")
• Solid experience with AWS services: EC2, S3, RDS, Lambda, ECS/EKS, API Gateway, CloudWatch, IAM; AWS certification (Solutions Architect – Associate/Professional) is a plus.
• Strong understanding of NFRs: designing for scalability (horizontal/vertical), high availability, fault tolerance, security (OWASP, data encryption), and observability (logging, monitoring, tracing).
• Experience with containerization and orchestration (Docker, Kubernetes) and CI/CD pipelines (Jenkins, GitLab CI, AWS CodePipeline).
• Familiarity with database technologies — both relational (PostgreSQL, MySQL, Oracle) and NoSQL (DynamoDB, MongoDB).
• Strong communication skills — ability to present architecture decisions to both technical and non-technical stakeholders.
• Experience in application/database migration or modernization projects (legacy to cloud-native).
• Exposure to event-driven architecture and streaming platforms (Kafka, Kinesis).
• Knowledge of Infrastructure as Code (Terraform, CloudFormation).
• Prior experience in a client-facing architect or presales role

Key Skills

Education

Any Graduate