Python Fullstack Developer

D:

Full-Stack Software Engineering services

• Lead the design, development, enhancement, and maintenance of software applications leveraging Python programming and modern cloud platforms
• Drive frontend, backend, and API development, with a focus on Python-based services and cloud-native architectures
• Enable seamless integration with internal systems, platforms, and data sources, including cloud-based services such as storage, compute, and data pipelines
• Build and deliver scalable, high-performance solutions using modern frameworks and cloud technologies (GCP/AWS/Azure/SF)
• Ensure high code quality through rigorous testing, debugging, and defect resolution across all application layers, including cloud deployments
• Contribute to and uphold engineering excellence by actively participating in code reviews and enforcing best practices in programming and cloud engineering
• Support deployment, CI/CD pipelines, and operational readiness, ensuring reliable and efficient releases in cloud environments
• Drive monitoring, performance optimization, and reliability improvements for applications operating in the cloud

Angular

Python

SQL

Cloud

Architecture

• Components, services, modules
  • Lifecycle hooks (ngOnChanges, ngOnDestroy)
  • Template vs reactive forms
  • Angular migration (v6 v16)
  • Handling 3rd-party compatibility
  • Standalone components vs modules
  • State management (BehaviorSubject, NgRx)
  • RxJS usage (switchMap, reactive flows)
  • Data sharing across components
  • Spinner/loading handling
  • Dashboard UI (charts, pagination, wizard forms)
  • API integration with backend
  • Angular vs React comparison
• FastAPI-based API development
  • Backend service implementation
  • PySpark data processing
  • Data migration (EDW EDW)
  • Data pipeline development
  • Handling large datasets
  • Data ingestion & preprocessing (image/audio)
  • AI/ML model selection (precision, recall)
  • Multi-agent LLM architecture
  • Chatbot integration
  • Backendfrontend integration
• SQL Server, PostgreSQL, MySQL, NoSQL
  • Joins, GROUP BY
  • CTEs vs Views
  • Tables vs Views
  • Window functions (LEAD, LAG)
  • Ranking & time-series analysis
  • Query writing for business problems
  • Indexing & partitioning
  • Query tuning (ClickHouse)
  • Stored procedure optimization
• AWS (Lambda, API Gateway, ECS, ECR, LB)
  • Azure (ADF, Databricks)
  • Azure DevOps / TFS pipelines
  • CI/CD (Jenkins, GitHub Actions)
  • Docker & Kubernetes
  • Deployment workflows
  • Cloud data pipelines
  • Managed AI services
  • Monitoring, logging, alerts, reruns
• J D:
  tem design (vendor management system)
  • Microservices architecture
  • REST API design principles
  • Real-time streaming vs APIs
  • WebSockets, async queue processing
  • Data architecture (Raw/Silver/Gold layers)
  • Global DB design (manufacturing/fabs)
  • Full-stack architecture (UI–API–DB)
  • Frontend vs backend logic decisions
  • Scalability & latency optimization
  • RAG, embeddings, vector DBs
  • LLM chunking strategies
  • Requirement analysis & solution designJ