Cloud Software Engineer

What we do: Design and develop cloud-native applications and microservices on AWS. Build scalable, highly available backend systems using modern architecture patterns. Develop and maintain RESTful/GraphQL APIs and event-driven services. Architect distributed systems with focus on reliability, security, scalability, and cost optimization.

Implement CI/CD pipelines, infrastructure-as-code, and automated testing. Build observability frameworks including logging, monitoring, and alerting. Optimize system performance, latency, throughput, and resource utilization. Integrate AI/ML or GenAI services (e.g., AWS Bedrock) where applicable to enhance automation or analytics.

Collaborate with cross-functional teams including platform, DevOps, data, QA, and business stakeholders. Core Technical Stack: Cloud & Infrastructure AWS (EC2, S3, Lambda, API Gateway, IAM, Cloud. Watch, SNS/SQS, DynamoDB, RDS) Containerization: Docker Orchestration: EKS/ECS/Fargate Infrastructure as Code: Terraform / Cloud.

Formation CI/CD: GitHub Actions, GitLab CI, Jenkins, Code. Pipeline Observability: Cloud. Watch, Data. Dog, Grafana Backend Development Python (FastAPI, Flask) or Java/Node.js REST / GraphQL API design Microservices architecture Event-driven systems Caching strategies (Redis, Elasti. Cache) Data & Messaging PostgreSQL, MySQL, DynamoDB Elasticsearch / Open.

Search Kafka / SNS / SQS Data pipelines (Airflow or equivalent) AI/ML (Nice Leverage, Not Primary) AWS Bedrock or Sage. Maker integration RAG-based services or LLM API integration Model API orchestration and monitoring Basic Qualifications: Bachelor s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field A minimum of 5 years of software development experience in production environments.

Strong hands-on experience with AWS cloud services. Experience designing and operating distributed systems. Proficiency in at least one backend language (Python, Java, or Node.js). Experience with containerized deployments (Docker + Kubernetes/ECS/EKS). Strong understanding of system design, scalability, and cloud security best practices.

Experience with CI/CD, automated testing, and infrastructure automation.

Preferred Qualifications

Master s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field Experience integrating AI/ML services into production systems. Experience with Databricks or large-scale data processing. Familiarity with automotive systems or enterprise PLM environments. Knowledge of event streaming architectures and high-throughput systems.

Experience in cost optimization for cloud workloads. What Success Looks Like: Highly available, scalable AWS services deployed to production. Reduced operational overhead through automation and cloud-native solutions. Optimized infrastructure cost and improved system performance. Clean, maintainable, well-documented code with strong test coverage.

Measurable business impact through reliable and efficient cloud platforms.