alex kanchalenka
AI Engineer
SQL
C#
JavaScript
TypeScript
Python
API Design: 4/10
Active 18 days ago
+375 (29) 6066697 Invite to interview
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Overview
Technical skills
Timeline
Roles
Overview
A backend-focused engineer at a solid middle level who independently builds end-to-end features integrating LLMs, embeddings and data ingestion across small services. The strongest proven skill is building RAG-style pipelines and LLM integrations, evidenced by the ingest and chat API routes that create embeddings, store chunks and perform similarity lookup (chatbot-builder/app/api/ingest/route.ts and chatbot-builder/app/api/chat/route.ts). The public code, however, lacks migration history, formal testing, structured observability and advanced scalability or transactional design.
Technical skills
SQL
C#
JavaScript
TypeScript• Middle
Python• Middle
Python
Requests
Databases
Databases
pgvector
PostgreSQL
Supabase
AI/ML
Cursor
LangChain
LLM
Ollama
LLM Apps
Semantic Search
AI/ML
Claude Code
Dify
Embeddings
Prompt Engineering
RAG
Claude
Frontend
React.js
Tailwind CSS
Vite
ESLint
PostCSS
autoprefixer
Next.js
DevOps
Docker
Git
Rest API
Nginx• 6y+
Timeline
Freelance AI / Python Developer
•
Middle
n8nmind.site
•
Full-Time
Develops AI/automation products including chatbots, LLM-based assistants, RAG systems, and agentic workflows. Owns the full workflow from requirements and architecture through implementation, deployment, and ongoing maintenance. Focuses on practical production-oriented solutions and prompt-driven retrieval and generation behavior.
Python
FastAPI
aiogram
Next.js
Supabase
PostgreSQL
pgvector
Dify
Embeddings
RAG
Prompt Engineering
Claude Code
Nginx
AI Agents
C#
Middle Backend Developer
Confidence: Medium API Engineer
A backend-focused engineer at a solid middle level who independently builds end-to-end features integrating LLMs, embeddings and data ingestion across small services. The strongest proven skill is building RAG-style pipelines and LLM integrations, evidenced by the ingest and chat API routes that create embeddings, store chunks and perform similarity lookup (chatbot-builder/app/api/ingest/route.ts and chatbot-builder/app/api/chat/route.ts). The public code, however, lacks migration history, formal testing, structured observability and advanced scalability or transactional design.
API Design
4/10
How well APIs are designed
API endpoints are implemented with input checks, status codes and auth gating, but lack versioning, idempotency design and formal error-contract documentation.
Evidence
chatbot-builder/app/api/chat/route.ts: parameter validation and NextResponse.json error responses
chatbot-builder/app/api/ingest/route.ts: input validation, status codes and chunk ingestion flow
chatbot-builder/lib/supabase/proxy.ts: session check and redirect-to-login logic
Data Layer & Database
3/10
Working with databases
Uses Supabase for storage and an RPC for vector matching; basic inserts and updates are present but there is no migration history, transaction handling, or explicit isolation/consistency controls in the code.
Evidence
chatbot-builder/app/api/ingest/route.ts: inserts document and chunk rows with embeddings into Supabase
chatbot-builder/app/api/chat/route.ts: calls supabase.rpc('match_chunks', ...) for similarity search
freelance-scout/scout/storage.py: CSV/JSON persistence for collected vacancies
Scalability & Performance
2/10
Handling load and speed
Some lightweight attention to performance (text chunking, deduplication, modest throttling) but no parallelization, batching for embeddings, caching strategy, or rate-limiting and no evidence of measured optimizations.
Evidence
chatbot-builder/app/api/ingest/route.ts: chunkText() and per-chunk sequential embedding creation
freelance-scout/scout/fetcher.py: fetch_all() deduplication and time.sleep(0.3) throttle
freelance-scout/main.py: CLI orchestration without batching or concurrency
System Architecture
3/10
Overall system structure
Code is modularly organized (API routes, lib/supabase, separate scout package for CLI), showing sensible boundaries for small projects, but lacks evidence of deliberate service decomposition, inter-service contracts, or config/secret lifecycle management beyond dotenv and env checks.
Evidence
freelance-scout/: clear package separation into scout.fetcher, scout.storage, scout.analyzer
chatbot-builder/app/api/: separated ingest and chat route implementations
chatbot-builder/lib/supabase/proxy.ts: centralized session handling helper
Security & Auth
3/10
Protecting data and access
Basic auth and env-secret checks are present (Supabase auth gating, API key checks), but input validation is minimal and there is no evidence of threat modeling, dependency auditing, or fine-grained auth/token lifecycle management.
Evidence
chatbot-builder/app/api/ingest/route.ts: returns 401 when supabase.auth.getUser() shows no user
chatbot-builder/lib/supabase/proxy.ts: session check and redirect for unauthenticated requests
freelance-scout/scout/analyzer.py: raises if ANTHROPIC_API_KEY is missing (env check)
Reliability & Observability
2/10
Stability and monitoring
Basic error handling and logging exist (try/catch and console.error, graceful 4xx/5xx responses); no structured logging, correlation ids, retries with backoff, health checks, or metrics/alerting artifacts were found.
Evidence
chatbot-builder/app/api/chat/route.ts: try/catch with console.error and 500 responses
chatbot-builder/app/api/ingest/route.ts: updates document status to 'error' on insert failure
freelance-scout/scout/fetcher.py: exception handling around external requests with printed messages
Expertise
Backend AI & LLM• Middle
Databases & Vector Storage• Middle
Python• Middle
Industries
Artificial Intelligence• Middle
Technologies
Databases
Python• Middle
C#
Rest API
Requests
Recommendations
- Improve data-layer robustness: add explicit schema migrations, transaction boundaries and a migration chain for evolving the chunks/documents schema.
- Harden observability and reliability: add structured logging with correlation ids, retries with exponential backoff for external calls, and basic metrics/health checks.
- Optimize embedding ingestion: batch embeddings, consider parallelism or worker queues and add limits/rate-limiting when calling LLM/embed APIs.
- Add tests and CI: unit tests for normalization and storage code, and integration tests for the API flows to raise confidence for production use.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Frontend Developer
Confidence: Medium App Engineer
A pragmatic frontend engineer at a middle level who builds polished MVPs and small production flows that integrate client UI with serverless APIs. The strongest proven skill is delivering end-to-end app-engineering for interactive web tools, evidenced by the embeddings ingestion and supabase-backed chat flow in q6066697/chatbot-builder/app/api/ingest/route.ts and app/api/chat/route.ts. There is limited evidence of test automation, measured performance work, or advanced state-machine patterns in public code.
UI Component Architecture
3/10
How interface parts are built
Basic component and styling patterns are present with small reusable hooks, but there is little evidence of a deliberate component-library architecture or advanced composition abstractions.
Responsive & Cross-browser
5/10
Works on all screens and browsers
Responsive layouts and dark-mode support are explicitly implemented with media queries and prefers-color-scheme, and Tailwind is configured for dark mode; the approach is pragmatic and cross-breakpoint.
Performance Optimization
3/10
Speed of the interface
Some practical performance-aware patterns exist such as in-memory caching and chunking of large inputs for embeddings, but there is no measured performance telemetry or advanced bundle/code-splitting work.
Evidence
q6066697/meeting-calendar/index.html: MEETINGS_CACHE in-memory cache to avoid repeated DOM/DB work
q6066697/chatbot-builder/app/api/ingest/route.ts: chunkText function to split large text before embedding
q6066697/meeting-calendar/index.html: comment explaining atomic RPC usage to avoid race conflicts
Accessibility & Semantics
3/10
Usable for everyone
Good form labeling, focus styles, and keyboard affordances exist for some interactions, but there is limited sign of systematic ARIA usage, focus management for custom widgets, or CI a11y tooling.
State Management & Data Flow
4/10
Managing data in the app
Server-state flows and error paths are handled end-to-end: supabase auth checks, RPC usage, insert-with-rollback semantics for documents and chunks, and local persisted state are implemented, but there are no advanced state machines, cancellation, or optimistic rollback patterns.
Evidence
q6066697/chatbot-builder/app/api/ingest/route.ts: supabase auth checks, insert document then chunks, status update on error
q6066697/chatbot-builder/app/api/chat/route.ts: embeddings then match_chunks RPC and defensive error handling
q6066697/lawyer-crm/src/useLocalStorage.ts: browser localStorage state with try/catch
UX & Visual Polish
5/10
Look and feel quality
UX attention is clear: empty states, inline error/success messaging, responsive controls, and helpful helper texts are present, producing a polished MVP experience for users.
Evidence
q6066697/meeting-calendar/index.html: empty state, inline msg box for errors and success, helpful hint text
q6066697/lawyer-crm/src/App.css: styled empty-state and counters, focused UI polish
q6066697/chatbot-builder/app/api/ingest/route.ts: document status transitions (processing, error, ready) for clear UX feedback
Expertise
React• Middle
Modern Web Frameworks• Middle
Frontend Architecture & Build Tools• Middle
Industries
Artificial Intelligence• Middle
Professional Services• Middle
Technologies
JavaScript
TypeScript• Middle
Tailwind CSS
Next.js
React.js
Vite
PostCSS
ESLint
autoprefixer
Recommendations
- Develop small-to-medium single page applications that require serverless integrations and real-time DB interactions (Supabase, RPCs, edge functions).
- Build internal tools and prototypes that need clear UX, form handling, persisted local state, and pragmatic error flows.
- Implement document ingestion and embedding pipelines or chatbot frontends where embedding chunking and server-side validation are needed.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Intern AI/ML Engineer
Confidence: Low LLM Engineer
A developer focused on building practical LLM-powered assistants and prompt-engineered workflows at a junior level. The strongest proven skill is designing end-user conversational flows and knowledge-base driven RAG concepts, evidenced by README descriptions and structured test scenarios (hr-interviewer-claude tests and Dify RAG pipeline notes). There is little to no original model code, experiment-tracking, deployment automation, or efficiency engineering present in the public human-authored files.
Model Architecture & Training
1/10
How well models are designed and trained
Very limited evidence of model architecture or training work in human-authored files; no model code, training loops, or hyperparameter choices present.
Evidence
dify-rag-assistant/README.md: RAG pipeline description mentions chunking and embedding model 'text-embedding-3-small'
hr-interviewer-claude/README.md: mentions system prompt and interview stages but no model training artifacts
Data Pipeline & Feature Engineering
1/10
How data is prepared for models
Minimal data-pipeline signals limited to high-level RAG steps and a knowledge_base text reference; no preprocessing code, dataset handling, or augmentation scripts found.
Evidence
dify-rag-assistant/README.md: outlines chunking and vectorization steps for RAG
dify-rag-assistant/README.md: references 'knowledge_base.txt' as source document for the RAG pipeline
Experimentation & Evaluation
2/10
How results are measured and tested
Some basic evaluation/testing artifacts exist at the scenario level (manual test runs and expected vs actual outcomes), but no reproducible experiment code, metrics logging, or experiment-tracking integrations.
Evidence
hr-interviewer-claude/tests/README.md: three test scenarios with expected outcomes and recorded scores
techhelper-ai-assistant/README.md: documented functional expectations and test-oriented descriptions
MLOps & Deployment
1/10
How models are shipped to production
Deployment/serving is described only at the platform level (Claude Projects, Dify); there is no MLOps code for model serialization, versioning, CI/CD or runtime monitoring in analyzed human-authored files.
Evidence
hr-interviewer-claude/README.md: states use of Claude Projects as the deployment/customization platform
dify-rag-assistant/README.md: describes building on Dify (low-code) but contains no deployment scripts
Computational Efficiency
1/10
How efficiently computing resources are used
No evidence of computational-efficiency engineering such as quantization, custom batching, profiling or GPU optimizations in the human-authored content.
Evidence
dify-rag-assistant/README.md: mentions embeddings and vector search but no efficiency or optimization artifacts
Research Depth & Innovation
1/10
Depth of research and new ideas
Research depth is minimal - the work is application- and prompt-focused with no paper implementations, custom layers, or novel algorithmic contributions present in the human-authored files.
Evidence
hr-interviewer-claude/README.md: emphasizes prompt structure and interview flow rather than novel model work
dify-rag-assistant/README.md: describes RAG approach at a high-level without research artifacts
Expertise
Conversational AI & Chatbots• Intern
Industries
Software• Intern
Technologies
AI/ML
LLM Apps
SQL
Cursor
LangChain
Claude
Claude Code
Ollama
Git
Docker
Nginx• 6y+
LLM
Semantic Search
Dify• mentioned only
LLM• mentioned only
Python• mentioned only
RAG• mentioned only
Recommendations
- Implement and publish the minimal runnable components (inference scripts and dataset preprocessing) so model and data engineering skills are visible.
- Add reproducible evaluation artifacts - unit tests, metric logging, and at least one experiment-tracking integration (W&B or MLflow) for candidate scoring and RAG retrieval.
- Include deployment and MLOps scaffolding - simple CI, model versioning, and a small inference service or container example to demonstrate production-readiness.
- Produce a short example of the retrieval pipeline code (chunking, embedding calls, vector store usage) to showcase practical engineering beyond descriptive READMEs.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: