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, CloudWatch, SNS/SQS, DynamoDB, RDS)

• Containerization: Docker

• Orchestration: EKS/ECS/Fargate

• Infrastructure as Code: Terraform / CloudFormation

• CI/CD: GitHub Actions, GitLab CI, Jenkins, CodePipeline

• Observability: CloudWatch, DataDog, Grafana

Backend Development

• Python (FastAPI, Flask) or Java/Node.js

• REST / GraphQL API design

• Microservices architecture

• Event-driven systems

• Caching strategies (Redis, ElastiCache)

Data & Messaging

• PostgreSQL, MySQL, DynamoDB

• Elasticsearch / OpenSearch

• Kafka / SNS / SQS

• Data pipelines (Airflow or equivalent)

AI/ML (Nice Leverage, Not Primary)

• AWS Bedrock or SageMaker 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.