| Description: ESSENTIAL EXPERIENCE AND JOB REQUIREMENTS
• Experience 7–10+ years building production backend systems; 5+ years hands-on with AWS
• Solid understanding and experience in AWS core services and emerging AWS technologies such as AWS Agent Core.
o Compute: Lambda, ECS, EKS o API & Integration: API Gateway, AppSync, Event Bridge, SQS, SNS
o Data & Storage: DynamoDB, Aurora (Postgres/MySQL), S3
o Networking & Security: VPC, IAM, KMS, Secrets Manager, CloudFront
o Observability: CloudWatch o Orchestration: Step Functions
o Authentication & Authorization: IAM Identity Centre, API Gateway authorizers and integration with Microsoft Azure Entra ID
• Extensive experience in designing and implementing microservices and event driven architectures
• Understanding and experience implementing AWS Well Architected Framework
• AWS certifications highly valued.
• Work effectively with various customer enterprise teams (security, AI governance, data platforms etc) to obtain required approvals and sign off on solution designs.
Hands on - The candidate must be fully hands on, actively contributing to coding, design, and implementation of backend services and AWS solutions.
Design & Build Agentic Backends: Define low level solution designs and implement backend services that orchestrate agent workflows using AWS primitives and AI frameworks.
Orchestration & Workflows: Implement robust orchestration for multi-step agent pipelines, including retries, timeouts, and compensating actions.
APIs & services: Build and integrate with secure, low latency APIs and event driven microservices with AWS services
Collaboration: Partner closely with a highly skilled, tight knit team and work cross functionally with security, data science, UX and customer enterprise platform teams to deliver high quality solutions.
KEY ACCOUNTABILITIES
• Design, build, and own scalable, resilient backend services on AWS to support mission-critical, production-grade AI systems.
• Architect and implement agentic AI platforms, including orchestration, state management, and interaction patterns for LLM-driven workflows.
• Integrate large language models securely and cost-effectively, applying best-practice patterns for governance, prompt management, and inference optimization.
• Ensure backend solutions meet enterprise standards for security, compliance, reliability, and operational readiness.
• Embed robust observability practices, including logging, monitoring, alerting, and tracing, to support production operations and continuous improvement.
• Drive cloud-native architecture decisions across AWS services (e.g., compute, storage, messaging, security) with a focus on scalability and cost control.
• Collaborate closely with product, UI, UX, data, and platform teams to deliver end-to-end solutions aligned to business outcomes.
• Identify and mitigate technical risks associated with AI integration, data handling, cloud architecture, and system performance.
• Apply strong engineering discipline through high-quality code, automated testing, CI/CD pipelines, and infrastructure-as-code.
• Act as a technical leader, contributing to architectural direction, engineering standards, and knowledge sharing across the team.
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