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
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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
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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.