Prajwal Uraw
Software Developer
C++
C#
JavaScript
C
PHP
Python
TypeScript
API Design: 4/10
Active 10 hours ago
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Overview
Technical skills
Timeline
Roles
Overview
A Python backend developer at a middle level specializing in building REST APIs with AI integrations and end-to-end backend features. The strongest proven skill is integrating LLM-based flows into backend logic and persisting results, demonstrated by the generate_ai_workout_plan, generate_ai_diet_plan and chatbot handlers in gym/backend/main.py that call genai/OpenAI and upsert plans into MongoDB. There is limited evidence of production-grade reliability, observability, schema migration history, and secure secret management in the public code.
Technical skills
C++
C#
JavaScript
C
PHP
Python• Middle
TypeScript• Middle
Python
Flask
Requests
Uvicorn
Pydantic
Asyncio
Django
FastAPI
Databases
MySQL
Oracle
MongoDB
PostgreSQL
AI/ML
AI Agents
LLM
DevOps
CI/CD
Git
GitHub Actions
Docker Compose
Containers
Rest API
QA
Pytest
Mobile
Clean Architecture
Timeline
Software Engineer (AI voice agents)
•
Middle
HAIEC
•
Full-Time
Architected backend systems for AI voice agents handling real inbound and outbound calls for US service businesses. Built event-driven backend logic with FastAPI and integrated LLMs with STT/TTS services and CRM connections. Delivered production-quality code that processes live customer interactions and improved overall system reliability.
FastAPI
AI Agents
LLM
Python
Content writing and research
•
Middle
Techverse
•
Full-Time
Created technical documentation including API guides and SOPs to clarify system behavior for engineers and users. Conducted business analysis and produced data-driven reports for strategic decisions. Documented cybersecurity and vulnerability assessment practices and supported implementation through website and Python automation scripts.
Python
Junior Backend Developer
•
Junior
Astrixel
•
Full-Time
Developed and maintained RESTful APIs with FastAPI and Django, owning features from design through deployment. Optimized MongoDB and PostgreSQL schemas to improve performance and API reliability. Supported scalable backend service development and corrected issues affecting concurrent behavior.
Rest API
FastAPI
Django
PostgreSQL
Python
Backend Developer (FastAPI, Django)
•
Middle
Astrixel
•
Internship
Built backend features and production APIs for an AI-focused platform using FastAPI and Django. Worked on webhook and messaging integrations, including WhatsApp and Instagram. Addressed real backend issues such as race conditions, async programming, and database performance improvements across MongoDB and PostgreSQL.
FastAPIsince 2025
Djangosince 2025
PostgreSQLsince 2025
AI Agentssince 2025
Pythonsince 2025
Middle Backend Developer
Confidence: High API Engineer
A Python backend developer at a middle level specializing in building REST APIs with AI integrations and end-to-end backend features. The strongest proven skill is integrating LLM-based flows into backend logic and persisting results, demonstrated by the generate_ai_workout_plan, generate_ai_diet_plan and chatbot handlers in gym/backend/main.py that call genai/OpenAI and upsert plans into MongoDB. There is limited evidence of production-grade reliability, observability, schema migration history, and secure secret management in the public code.
API Design
4/10
How well APIs are designed
Solid REST API design using FastAPI and Pydantic models with JWT-based auth and dependency injection, but missing versioning, idempotency, pagination and consistent error contract hygiene.
Data Layer & Database
3/10
Working with databases
Uses async MongoDB client and upserts appropriately but no migration history, transaction handling, or explicit indexing/SQL tuning are present.
Scalability & Performance
2/10
Handling load and speed
Asynchronous IO and motor are used which helps concurrency, but there is no caching, rate limiting, queueing, or measured performance tuning present.
System Architecture
2/10
Overall system structure
Project shows basic service boundaries (demo Flask microservices and a FastAPI backend) and dotenv config, but no clear service decomposition rationale, secret management, or config separation for prod-grade deployment.
Security & Auth
3/10
Protecting data and access
Basic authentication and cryptography choices are present (JWT, bcrypt, HTTPBearer) and input validation via Pydantic, but there are insecure defaults and missing token lifecycle management and secret hygiene.
Reliability & Observability
2/10
Stability and monitoring
Startup/shutdown hooks and try/except error handling exist, but there is little evidence of retries/backoff, timeouts for external AI calls, structured logging, metrics or graceful degradation patterns.
Expertise
Backend AI & LLM• Middle
Microservices & API Architecture• Middle
Python• Middle
Industries
Sports• Middle
Technologies
PHP
MongoDB
DevOps• mentioned only
Stack• mentioned only
Recommendations
- Develop AI-backed backend services and prototypes that integrate LLM calls with persistent storage and clear input/output contracts (leverage the existing genai integration patterns).
- Harden authentication and secrets: implement secure secret management, token refresh/revocation, and avoid default secret values in code.
- Improve reliability and observability by adding timeouts, retries with backoff for external calls, structured logging with correlation ids, and basic metrics/alerts.
- Convert AI call workflows into asynchronous or queue-driven tasks to decouple latency-sensitive endpoints from long-running model calls and add caching where appropriate.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle QA Engineer
Confidence: Medium API QA Engineer
API-focused Middle-level engineer specializing in building FastAPI backends with AI integrations for fitness-oriented applications. The strongest proven skill is backend API design and async data integration as demonstrated in the gym backend (main.py) implementing JWT auth, async MongoDB startup/shutdown, and endpoints that generate and persist AI-driven workout and diet plans. Public code does not show mature test infrastructure, contract or property-based testing, or performance/load testing artifacts.
Test Automation Frameworks
2/10
Building automated tests
Minimal test automation exists: a couple of simple integration-style tests using requests against local Flask services, but there is no fixture isolation, no parallelization, no test factories or mock servers.
Evidence
devops-project/tests/test_service1.py: requests.get("http://localhost:5000") asserting status and exact body
devops-project/tests/test_service2.py: requests.get("http://localhost:5001") asserting status and exact body
Test Coverage & Strategy
1/10
What and how to test
Test strategy is limited to a small number of happy-path integration checks; there is no visible negative-path, boundary, property-based, or mutation testing.
Evidence
devops-project/tests/test_service1.py: single happy-path assertions
devops-project/tests/test_service2.py: single happy-path assertions
API & Integration Testing
3/10
Testing how parts work together
Strongest area is API and backend integration: an async FastAPI backend with Pydantic models, JWT auth, async MongoDB usage and a typed frontend ApiClient; however there is no formal contract testing or schema-driven test tooling visible.
Evidence
gym/backend/main.py: FastAPI endpoints with create_access_token, get_current_user dependency, async motor usage (generate_ai_workout_plan, get_workout_plan)
gym/src/lib/api.ts: ApiClient.request with token management and HTTP error handling
Performance & Load Testing
Testing speed under load
Not evidenced in public code
Bug Reporting & Analysis
Finding and describing bugs
Not evidenced in public code
CI Test Integration
Running tests automatically
Not evidenced in public code
Expertise
SDET & Test Engineering• Middle
Industries
Lifestyle• Middle
Technologies
Python• Middle
TypeScript• Middle
Flask
FastAPI
CI/CD
Asyncio
Pytest
Pydantic
Uvicorn
Requests
DevOps• mentioned only
Recommendations
- Develop and own backend API features and AI-integration endpoints, including secure token management and async DB flows.
- Implement robust API test coverage: add contract/schema validation (schemathesis or Pact), negative-path tests, and test fixtures with testcontainers or local DB instances.
- Build CI test integration and artifact handling: add pipeline workflows, JUnit/XML reporting, selective test runs, and per-test retry/quarantine policies.
- Improve test quality by adding boundary and adversarial tests for AI outputs, and add JSON schema validation and error-path tests for third-party model failures.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Junior DevOps Engineer
Confidence: Medium Generalist
Junior DevOps generalist focused on building and validating small containerized Python microservices. The strongest proven skill is basic containerization and local integration testing using Flask apps, Dockerfiles, docker-compose, and pytest-style HTTP tests. There is little or no evidence of CI/CD automation, infrastructure-as-code for cloud environments, observability, or production-grade deployment and resilience practices.
CI/CD Pipelines
1/10
Automated build and deploy
Very light CI/CD evidence: local test invocations and a docker-compose orchestration file are present but there is no pipeline definition, reusable workflows, gating or artifact management.
Evidence
devops-project/docker-compose.yml
devops-project/tests/test_service1.py
Infrastructure as Code
1/10
Managing servers with code
Minimal infrastructure-as-code: only a docker-compose.yml for local composition is present; no Terraform/CloudFormation/Ansible, no remote state or environment separation.
Evidence
devops-project/docker-compose.yml
Containerization & Orchestration
2/10
Working with containers
Basic containerization demonstrated by simple Dockerfiles and docker-compose; lacks multi-stage builds, non-root users, tuned resource requests/limits justification, health/readiness probes, or orchestration features for resilience.
Evidence
devops-project/service1/Dockerfile
devops-project/service2/Dockerfile
devops-project/docker-compose.yml
Observability & Monitoring
Watching system health
Not evidenced in public code
Reliability & Incident Response
Keeping systems up
Not evidenced in public code
Cloud & Cost Optimization
Smart use of the cloud
Not evidenced in public code
Technologies
Containers
Docker Compose
DevOps• mentioned only
Recommendations
- Implement a real CI pipeline (Jenkinsfile or GitHub Actions) with reusable steps, caching, and an automated test-and-deploy gate to demonstrate pipeline engineering skills.
- Harden container builds: use multi-stage Dockerfiles, run as non-root, add minimal image size optimization, and add health/readiness endpoints with tuned probes.
- Add observability: expose Prometheus metrics, structured logging/tracing, and author at least one alerting rule or dashboard-as-code to show SLO/alerting understanding.
- Introduce simple IaC (Terraform or CloudFormation) with remote state and at least two environment configurations (dev/prod) to show state management and environment separation.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: