Cloud Software Engineer

What we do:

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Design and develop cloud-native applications and microservices on AWS.

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Build scalable, highly available backend systems using modern architecture patterns.

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Develop and maintain RESTful/GraphQL APIs and event-driven services.

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Architect distributed systems with focus on reliability, security, scalability, and cost optimization.

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Implement CI/CD pipelines, infrastructure-as-code, and automated testing.

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Build observability frameworks including logging, monitoring, and alerting.

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Optimize system performance, latency, throughput, and resource utilization.

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Integrate AI/ML or GenAI services (e.g., AWS Bedrock) where applicable to enhance automation or analytics.

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Collaborate with cross-functional teams including platform, DevOps, data, QA, and business stakeholders.

Core Technical Stack:

Cloud & Infrastructure

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AWS (EC2, S3, Lambda, API Gateway, IAM, CloudWatch, SNS/SQS, DynamoDB, RDS)

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Containerization: Docker

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Orchestration: EKS/ECS/Fargate

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Infrastructure as Code: Terraform / CloudFormation

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

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Observability: CloudWatch, DataDog, Grafana

Backend Development

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

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REST / GraphQL API design

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Microservices architecture

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Event-driven systems

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Caching strategies (Redis, ElastiCache)

Data & Messaging

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PostgreSQL, MySQL, DynamoDB

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Elasticsearch / OpenSearch

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Kafka / SNS / SQS

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Data pipelines (Airflow or equivalent)

AI/ML (Nice Leverage, Not Primary)

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AWS Bedrock or SageMaker integration

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RAG-based services or LLM API integration

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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.

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Strong hands-on experience with AWS cloud services.

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Experience designing and operating distributed systems.

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Proficiency in at least one backend language (Python, Java, or Node.js).

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Experience with containerized deployments (Docker + Kubernetes/ECS/EKS).

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Strong understanding of system design, scalability, and cloud security best practices.

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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.

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Experience with Databricks or large-scale data processing.

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Familiarity with automotive systems or enterprise PLM environments.

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Knowledge of event streaming architectures and high-throughput systems.

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Experience in cost optimization for cloud workloads.

What Success Looks Like:

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Highly available, scalable AWS services deployed to production.

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Reduced operational overhead through automation and cloud-native solutions.

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Optimized infrastructure cost and improved system performance.

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Clean, maintainable, well-documented code with strong test coverage.

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Measurable business impact through reliable and efficient cloud platforms.