Batte Akhsam
7+ years exp
20+ projects
C#
Node JS
TypeScript
Python
JavaScript
Data Layer & Database: 5/10
System Architecture: 5/10
API Design: 4/10
Active 9 days ago
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Overview
Technical skills
Timeline
Roles
Overview
An LLM-driven backend engineer (senior-level) focused on API-first systems that integrate generative AI for operational decisioning. The strongest proven skill is building LLM orchestration and resilient DB integration as shown by the ADK-based FraudShield agent, the Gemini client with robust JSON sanitisation and the MongoDB MCP client with reconnect logic. The public code lacks production-grade auth/session management, secrets lifecycle and documented migrations or test coverage.
Technical skills
C#• Senior • 6y+ • 4 projects
Node JS• Senior • 6y+ • 20+ projects
TypeScript• Senior • 5y+ • 10+ projects
Python• Senior • 4y+
JavaScript
Node JS
Express• 6y+ • 20+ projects
Python
FastAPI
Pydantic
Databases
Firestore
MySQL• 7y+ • 20+ projects
PostgreSQL• 6y+ • 10+ projects
MongoDB• 6y+ • 20+ projects
Frontend
Tailwind CSS
Vite
Mapbox GL JS
React Router
Angular• 6y+ • 20+ projects
React.js• 4y+ • 4 projects
Next.js• 3 projects
DevOps
GCP
Rest API
Docker• 7y+ • 20+ projects
containerd• 6y+ • 5+ projects
CI/CD• 6y+ • 20+ projects
DigitalOcean• 3y+ • 20+ projects
Azure AKS• 1 project
Design
Adobe Photoshop• 7y+ • 20+ projects
AI/ML
AI Agents• 5+ projects
Anthropic SDK• 4 projects
Claude Code• 5+ projects
Copilot
LLM
Timeline
Solutions Engineer
•
Middle
EFRISBuddy
•
Contractor
Team Lead
•
Lead
Reveloop Tech. Systems
•
Contractor
EFRISBuddy Developer Portal
Backend
EFRISBuddy
Mobile app
GridGuard
AI/ML
AFYAScan
AI/ML
Software Engineering Manager
•
Lead
Kumusoft Solutions Ltd
•
Full-Time
Software Engineer
•
Middle
Kumusoft Solutions Ltd
•
Full-Time
Senior Backend Developer
Confidence: High API Engineer
An LLM-driven backend engineer (senior-level) focused on API-first systems that integrate generative AI for operational decisioning. The strongest proven skill is building LLM orchestration and resilient DB integration as shown by the ADK-based FraudShield agent, the Gemini client with robust JSON sanitisation and the MongoDB MCP client with reconnect logic. The public code lacks production-grade auth/session management, secrets lifecycle and documented migrations or test coverage.
API Design
4/10
How well APIs are designed
API surface is pragmatic with typed request models, WebSocket streaming and a clear REST contract, but lacks versioning, idempotency, and a hardened auth/session strategy.
Data Layer & Database
5/10
Working with databases
Custom async DB adapter with connection/session management, Extended JSON parsing and aggregation support shows deliberate DB-wrapper design; no migration history or complex transactional boundaries are present.
Evidence
axoblade/fraud-shield-agent/agent/mongo_mcp.py: MongoDBMCPClient with _ensure_connected, _call_tool, _parse_extended_json, find/aggregate/insert_one/update_one/count
axoblade/fraud-shield-agent/agent/adk_agent.py: usage of aggregate and find in tool implementations (_tool_history, _tool_baseline, _tool_mule)
Scalability & Performance
4/10
Handling load and speed
Some scalability and resilience work present: persistent MCP session, reconnect/retry logic and async WebSocket streaming, but no caching/invalidations, no rate limiting or measured performance tuning.
System Architecture
5/10
Overall system structure
Reasoned module decomposition (agent, actions, DB client, api) with fallbacks and separation of concerns; architecture is pragmatic for an LLM-driven backend but lacks documented service contracts and formal config/secret management beyond dotenv.
Security & Auth
3/10
Protecting data and access
Some input validation (pydantic models) and env-based secret usage are present, but auth is a dummy in-memory token scheme, secrets/sms wiring are placeholders, and there is limited hardening against common API threats.
Reliability & Observability
4/10
Stability and monitoring
Logging, retry patterns and heuristic fallbacks exist and the MCP client retries and sanitises responses, but there is limited structured tracing, correlation ids, circuit breakers, or explicit graceful shutdown handling.
Expertise
Backend AI & LLM• Senior
Python• Middle
Microservices & API Architecture• Middle
Industries
Financial Services• Senior
Health Care• Middle
Energy & Utilities• Middle
Technologies
Python• Senior • 4y+
Node JS• Senior • 6y+ • 20+ projects
MongoDB• 6y+ • 20+ projects
Firestore
Express• 6y+ • 20+ projects
FastAPI
Pydantic
Cloud• mentioned only
Recommendations
- Develop LLM-powered backend services that require prompt engineering, tool orchestration and deterministic fallbacks - e.g., risk engines and automation pipelines.
- Build asynchronous API backends with FastAPI and custom DB adapters where session management and reconnection semantics are required.
- Implement real-time streaming features and dashboards using WebSockets for monitoring and replayable event processing.
- Integrate generative AI safely by adding response validation layers, strict secret handling, and end-to-end test coverage for model-dependent logic.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle QA Engineer
Confidence: Medium Generalist
Full-stack engineer (middle) focused on building web and mobile integrations that combine cloud services, telecom APIs, and AI for real-world problems. The strongest proven skill is integrating external services end-to-end as evidenced by the Node/TypeScript Nokia API proxy server (server.ts) and Google GenAI integration (geminiService.ts and afya-scan gemini functions). There is little to no public evidence of automated testing, CI integration, contract tests, or thorough QA infrastructure in the analyzed human-authored code.
Test Automation Frameworks
Building automated tests
Not evidenced in public code
Test Coverage & Strategy
What and how to test
Not evidenced in public code
API & Integration Testing
Testing how parts work together
Not evidenced in public code
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
Industries
Health Care• Middle
Telecommunications• Middle
Technologies
Rest API
GCP
Cloud• mentioned only
Recommendations
- Develop production-grade API integration and backend services that connect telecom or third-party provider APIs with robust error handling and observability.
- Build mobile and web features that rely on cloud AI inference pipelines, especially for health-triage or geofencing workflows, where the developer already has domain code.
- Add test automation and QA infrastructure: unit tests, contract/schema validation for external APIs, and CI pipelines that collect artifacts and guardrails for sensitive domains.
- Harden sensitive AI and medical paths with validation, input sanitization, and documented failure modes before deploying in clinical or safety-critical settings.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Intern Frontend Developer
Confidence: Low UI Engineer
Frontend developer (junior) focused on web UI work and integration with modern JS toolchains. The strongest proven artifact is multi-project JavaScript/TypeScript frontend usage and dependencies (Next.js/Vite/React entry points and package.json manifests). There is little original application code or engineering-level frontend architecture visible and most public UI assets are template/boilerplate rather than custom components or state-machine logic.
UI Component Architecture
How interface parts are built
Not evidenced in public code
Responsive & Cross-browser
Works on all screens and browsers
Not evidenced in public code
Performance Optimization
Speed of the interface
Not evidenced in public code
Accessibility & Semantics
Usable for everyone
Not evidenced in public code
State Management & Data Flow
Managing data in the app
Not evidenced in public code
UX & Visual Polish
Look and feel quality
Not evidenced in public code
Verified artifacts
Expertise
React• Intern
Modern Web Frameworks• Intern
Industries
Health Care• Middle
Telecommunications• Middle
Technologies
Tailwind CSS
Vite
Mapbox GL JS
React Router
Cloud• mentioned only
Recommendations
- Develop UI-driven pages and components using React + TypeScript for single-page apps and Next.js pages, focusing on component composition and tests.
- Implement mapping and location features (Mapbox) or data-driven dashboards where interactive UI and responsive behavior are required.
- Build small end-to-end features (forms, offline-friendly views, RDT result pages) with clear async state handling, tests, and accessibility improvements.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: