Santhosh Jallu

Backend • Senior • 27 years • Hyderabad, India • UTC+05:30 • Work permits: India • Available now

Software Developer

4+ years exp

2+ projects

Java

Python

Node JS

JavaScript

API Design: 5/10

Data Layer & Database: 5/10

Scalability & Performance: 5/10

Active 3 days ago

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Overview

Technical skills

Timeline

Roles

Overview

Backend engineer focusing on microservices and distributed patterns (Senior level) with practical strength in building resilient service-to-service integrations. The strongest proven skill is designing resilient inter-service RPC and event flows with concrete artifacts in ProductServiceGrpc.java (timeouts, retries, virtual-thread executor) and OrderEventService/OrderEventPublisher testable with Testcontainers. The public code does not evidence large-scale production deployment automation, formal schema migration histories, or comprehensive observability/metrics pipelines.

Technical skills

Languages

Java

Python

Node JS

JavaScript

Node JS

Express

Axios

Bcrypt

Mongoose

Electron

Java

Spring Boot

Spring Data JPA

Testcontainers

Python

FastAPI

SQLAlchemy

Pydantic

Other

MySQL

Claude Code

OpenAI Codex

React.js

RabbitMQ

React Router

Dotenv

GitHub

Flyway

Timeline

Project Engineer • Middle

Wipro Limited • Full-Time

Jun 2021 to Nov 2023 2 Years 5 Months Hyderabad In office

Developed backend APIs and contributed to deployable cloud functions. Supported data processing and integration tasks to ensure correct data output for downstream use. Worked within a delivery process to move changes to production.

Senior Member of Technical Staff • Lead

Oracle India Limited • Full-Time

Bengaluru In office

Worked on testing and validation activities to ensure system behavior in staging and production environments. Supported release execution by coordinating validation activities during deployments. Collaborated with QA teams to verify expected outcomes and troubleshoot issues as needed.

Oracle

Senior Backend Developer Confidence: High Distributed Systems

API Design

5/10

How well APIs are designed

APIs show deliberate design choices including pagination, consistent error handling and gRPC-to-HTTP error mapping, but lack explicit versioning and advanced idempotency handling.

Evidence

microservices-learning-path/catalog-service/src/main/java/dev/silvercapes/catalogservice/service/ProductService.java

microservices-learning-path/catalog-service/src/main/java/dev/silvercapes/catalogservice/service/ProductServiceGrpc.java

microservices-learning-path/catalog-service/src/main/java/dev/silvercapes/catalogservice/exceptions/GlobalExceptionHandler.java

Data Layer & Database

5/10

Working with databases

Data layer uses JPA repositories, transactional boundaries and integration test infra with Testcontainers, but there is no migration history or explicit schema-evolution artifacts in the provided files.

Evidence

microservices-learning-path/catalog-service/src/main/java/dev/silvercapes/catalogservice/repository/ProductRepository.java

microservices-learning-path/catalog-service/src/main/java/dev/silvercapes/catalogservice/service/ProductService.java

microservices-learning-path/order-service/src/test/java/dev/silvercapes/orderservice/TestcontainersConfiguration.java

Scalability & Performance

5/10

Handling load and speed

Performance and resilience patterns are present (retries, timeouts, virtual-thread executor and messaging), but there is no evidence of measured load testing, sophisticated caching with invalidation, or tuned SQL.

Evidence

microservices-learning-path/catalog-service/src/main/java/dev/silvercapes/catalogservice/service/ProductServiceGrpc.java

microservices-learning-path/order-service/src/main/java/dev/silvercapes/orderservice/config/RabbitMQConfig.java

microservices-learning-path/catalog-service/src/main/java/dev/silvercapes/catalogservice/service/ProductService.java

System Architecture

5/10

Overall system structure

Clear microservice decomposition, service/repository/controller separation and event-driven components are present, showing intentional architecture choices though the system is a small-scale learning project rather than a multi-team production decomposition.

Evidence

microservices-learning-path/catalog-service/src/main/java/dev/silvercapes/catalogservice

microservices-learning-path/order-service/src/main/java/dev/silvercapes/orderservice/publishers/OrderEventPublisher.java

flow_manager/services/flow_execution_service.py

Security & Auth

4/10

Protecting data and access

Basic auth/authn practices exist (bcrypt password hashing, JWT issuance, validation annotations), but token lifecycle, refresh/revocation, role enforcement and hardened input boundary checks are not fully implemented in the shown code.

Evidence

React-app/backend/routes/AuthRoutes.js

React-app/backend/models/User.js

microservices-learning-path/order-service/src/main/java/dev/silvercapes/orderservice/dto/CreateOrderRequestDTO.java

Reliability & Observability

5/10

Stability and monitoring

Reliability patterns are visible: retries, timeouts, scheduled publishing, and integration tests with Testcontainers/WireMock; however, advanced resilience (circuit breakers with operational tuning), systematic observability (metrics/alerts) and graceful shutdown handling are not visible in the examined files.

Evidence

microservices-learning-path/catalog-service/src/main/java/dev/silvercapes/catalogservice/service/ProductServiceGrpc.java

microservices-learning-path/order-service/src/main/java/dev/silvercapes/orderservice/service/OrderEventService.java

microservices-learning-path/order-service/src/test/java/dev/silvercapes/orderservice/AbstractIntegrationTests.java

Expertise

Java• Senior

Microservices & API Architecture• Senior

Messaging & Real-time• Senior

Node.js• Junior

Python• Middle

Industries

Commerce• Senior

Technologies

Python• since 2025 • Senior

Java• since 2025 • Senior

Node JS• Senior

Express

Spring Boot

SQLAlchemy

RabbitMQ

FastAPI

Bcrypt

Spring Data JPA

Testcontainers

Pydantic

Mongoose

Axios

Dotenv

Recommendations

Lead development of small-to-medium event-driven microservices and gRPC endpoints where retries, timeouts and message durability matter.
Implement and own integration test suites and local CI jobs that use Testcontainers and WireMock for reliable contract testing.
Build workflow/orchestration features or internal developer tooling that combine FastAPI services with SQLAlchemy-backed stores and clear service boundaries.
Harden auth flows and token lifecycle (refresh, revocation) and add structured observability (metrics, traces, alerts) to evolve the projects toward production readiness.

Repositories

The developer's experience in this domain has been verified based on AI analysis of the following repositories:

microservices-learning-path flow_manager React-app

Middle Frontend Developer Confidence: Medium Fullstack

Fullstack developer (mid-level) with practical experience building small React frontends, a simple Express auth API, and an Electron utility app; the strongest area is building interactive frontend flows and small fullstack integrations. The clearest proven skill is frontend application development using React components and controlled forms, as shown by the NewExpenseForm, Expenses composition in App.js, and the Login/Register pages calling API endpoints. There is limited evidence of advanced engineering practices such as automated testing, performance measurement, complex state machines, or production-grade deployment pipelines.

UI Component Architecture

3/10

How interface parts are built

Basic component composition and prop-driven design in React with small reusable components but no custom design system or advanced composition patterns.

Evidence

React-tutorials/react-app/src/App.js: top-level state and addExpenseHandler using functional state update

React-tutorials/react-app/src/components/Expenses/Expenses.js: Expenses component composing ExpenseItem, Card and ExpensesFilter

React-tutorials/react-app/src/components/NewExpenses/NewExpenseForm.js: controlled form component with local state and submit handler

Responsive & Cross-browser

2/10

Works on all screens and browsers

Simple responsive rules and layout adjustments via media queries; no advanced responsive techniques or multi-directional i18n/RTL support.

Evidence

React-tutorials/react-app/src/components/Expenses/ExpenseItem.css: @media (min-width: 580px) rules

Electron_projects/screen_recorder_app/src/index.html: sensible meta and single-page layout for Electron UI

Performance Optimization

1/10

Speed of the interface

Little to no targeted performance work or measurements; uses standard build scripts provided by tooling without custom code-splitting or virtualization.

Evidence

React-tutorials/react-app/package.json: build script using react-scripts

Electron_projects/screen_recorder_app/src/index.js: straightforward BrowserWindow creation without resource- or performance-focused options

Accessibility & Semantics

2/10

Usable for everyone

Forms and inputs include semantic labels and required attributes, but there is no explicit ARIA on custom widgets or focus management for complex interactions.

Evidence

React-app/frontend/src/pages/Login.js: form fields with label htmlFor and required attributes

React-app/frontend/src/pages/Register.js: labeled inputs with client-side password confirmation and message feedback

State Management & Data Flow

3/10

Managing data in the app

Appropriate basic client-server flows using axios and a simple backend with hashed passwords and JWTs; state is local with prop drilling and no advanced server-state caching or cancellation.

Evidence

React-app/frontend/src/pages/Login.js: API.post('/auth/login') and handling of loading/error states

React-app/backend/routes/AuthRoutes.js: password hashing with bcrypt and JWT issuance

React-tutorials/react-app/src/App.js: top-level expenses state and functional setState pattern

UX & Visual Polish

3/10

Look and feel quality

Visible attention to UX details like loading states, success/error messages, and toggled button text in the Electron recorder, but lacking advanced perceived-performance or undo patterns.

Evidence

React-app/frontend/src/pages/Register.js: loading state and user-facing messages during registration

React-app/frontend/src/pages/Login.js: disabled submit and message styling for success/error

Electron_projects/screen_recorder_app/src/render.js: start/stop button text changes and save dialog flow

Expertise

React• Middle

HTML & CSS• Middle

PWA & Web APIs• Middle

Technologies

Electron

React.js

React Router

Recommendations

Build small fullstack prototypes with React frontends and Express APIs where you own the end-to-end flow, such as dashboards or admin tools.
Develop desktop utilities using Electron that integrate browser Web APIs (media capture, file I/O) and polish their UX and error handling.
Work on component libraries or a design-system subset to strengthen component architecture, composition patterns and reusability.
Add automated tests and CI, and explore server-state solutions (React Query/SWR) and request cancellation patterns to improve robustness.

Repositories

The developer's experience in this domain has been verified based on AI analysis of the following repositories:

Electron_projects React-tutorials React-app

Intern AI/ML Engineer Confidence: Low Generalist

Entry-level Python engineer focused on simple LLM-agent demos and CLI utilities. The strongest proven skill is writing small application logic and utilities as shown by calculator/pkg/calculator.py and call_function.py. There is no evidence of custom model training, production-grade deployment, MLOps, or substantial experiment and evaluation infrastructure in the human-authored code.

Model Architecture & Training

How well models are designed and trained

Not evidenced in public code

Data Pipeline & Feature Engineering

How data is prepared for models

Not evidenced in public code

Experimentation & Evaluation

How results are measured and tested

Not evidenced in public code

MLOps & Deployment

How models are shipped to production

Not evidenced in public code

Computational Efficiency

How efficiently computing resources are used

Not evidenced in public code

Research Depth & Innovation

Depth of research and new ideas

Not evidenced in public code

Recommendations

Build small end-to-end prototypes - CLI tools, simple agent workflows and helper utilities where quick iteration matters.
Invest time in learning and implementing at least one end-to-end ML project with a reproducible training loop, validation split and logged experiments (W&B or MLflow).
Add unit tests, CI configuration, and basic deployment scripts to demonstrate ownership of production-readiness and MLOps practices.

Repositories

The developer's experience in this domain has been verified based on AI analysis of the following repositories:

agentic-ai