Stanislav Feoktistov
Python
Active 3 days ago
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Overview
Technical skills
Roles
Overview
Computational math software engineer (Middle) focusing on numerical algorithms and interactive educational tools. The strongest proven skill is algorithmic implementation for finite fields and polynomial arithmetic as shown by the karatsuba_multiply routine in core/elements/functions.py and the parallel irreducible-polynomial search in core/find_irreducible_poly.py. The codebase does not demonstrate ML model development, model training, production ML serving, or advanced MLOps pipelines.
Technical skills
Python• Middle
Python
Uvicorn
FastAPI
Databases
SQLite
AI/ML
NumPy
Streamlit
Transformers
Scikit-learn
Pillow
OpenCV
Torchvision
timm
Accelerate
Gradio
NLTK
Analytics
Matplotlib
Middle AI/ML Engineer
Confidence: Medium Generalist
Computational math software engineer (Middle) focusing on numerical algorithms and interactive educational tools. The strongest proven skill is algorithmic implementation for finite fields and polynomial arithmetic as shown by the karatsuba_multiply routine in core/elements/functions.py and the parallel irreducible-polynomial search in core/find_irreducible_poly.py. The codebase does not demonstrate ML model development, model training, production ML serving, or advanced MLOps pipelines.
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
3/10
How efficiently computing resources are used
Concrete algorithmic and performance-oriented code is present (optimized multiplication, and parallel irreducible-polynomial search), showing attention to computational efficiency and parallelism.
Evidence
my_galios/core/elements/functions.py: karatsuba_multiply
my_galios/core/find_irreducible_poly.py: find_irreducible_polynomials_batch (uses ProcessPoolExecutor)
Research Depth & Innovation
3/10
Depth of research and new ideas
Original mathematical algorithm implementations and irreducibility tests demonstrate problem understanding and some research-level coding of numerical algorithms.
Evidence
my_galios/core/elements/irreducibility_test.py: is_irreducible_benor
my_galios/core/elements/functions.py: karatsuba_multiply
Industries
Education• Middle
Science & Engineering• Middle
Software• Middle
Technologies
Streamlit
Claude• mentioned only
Claude Code• mentioned only
LLM• mentioned only
Prompt Engineering• mentioned only
Pydantic• mentioned only
Recommendations
- Develop interactive educational or research tools for computational algebra and number theory using Streamlit and the existing Galois field core.
- Enhance the algorithm library into a reusable Python package with clear API, benchmarking, and packaging for PyPI to support numerical research or coursework.
- Extend numerical routines with rigorous unit benchmarks and CI performance tests, and add documented examples for reproducible usage and teaching.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Data Scientist
Confidence: Medium ML Practitioner
A middle-level ML practitioner focused on prototyping and fine-tuning transformer-based vision-language models and implementing applied cryptographic protocols. Strongest proven skill is implementing a Private Set Intersection pipeline with homomorphic-crypto components - see server_logic.py and client_logic.py which implement TenSEAL-based PSI with cuckoo hashing and polynomial encoding. Public work shows practical prototyping and scripting but lacks systematic testing, production hardening and thorough statistical analysis of model results.
Statistical Rigor
2/10
Correct use of statistics
Minimal statistical rigor: basic train/validation split and loss plotting but no hypothesis tests, uncertainty quantification, or careful leakage checks.
Evidence
suai_2024_practice/fine_tuning.ipynb
suai_2024_practice/scripts/preparation.py
Data Wrangling & Cleaning
4/10
Preparing and cleaning data
Practical data wrangling for ML: dataset grouping, image copying, and train/valid split with some defensive checks when copying images.
Evidence
suai_2024_practice/scripts/preparation.py
suai_2024_practice/fine_tuning.ipynb
Exploratory Analysis & Visualization
3/10
Exploring and visualizing data
Lightweight EDA and visualization: loss plotting utilities and some notebook outputs, but no structured exploratory analysis or narrative-driven findings.
Evidence
suai_2024_practice/scripts/finetuning.py
suai_2024_practice/fine_tuning.ipynb
Predictive Modeling
3/10
Building models that predict
Predictive modeling competence at a prototyping level: notebooks and training orchestration are present, but model validation, CV, calibration and error analysis are limited.
Evidence
suai_2024_practice/fine_tuning.ipynb
suai_2024_practice/scripts/finetuning.py
Business Insight & Impact
1/10
Turning analysis into business value
Little evidence of business impact framing or cost/benefit analysis; README notes honest negative training results but not tied to business metrics.
Evidence
suai_2024_practice/README.md
Reproducibility & Notebook Hygiene
3/10
Clean, repeatable analysis
Basic reproducibility practices exist - requirements.txt, reusable scripts and helper functions - but full pipeline automation, CI, pinned envs and DVC-style data versioning are missing.
Evidence
suai_2024_practice/requirements.txt
suai_2024_practice/fine_tuning.ipynb
Expertise
Analytics• Middle
Technologies
Python• Middle
OpenCV
FastAPI
Scikit-learn
Matplotlib
Gradio
Accelerate
timm
Transformers
NumPy
NLTK
Uvicorn
Pillow
Torchvision
Recommendations
- Develop and productionize end-to-end model fine-tuning pipelines and training orchestration (use preparate_data + fine_tuning.ipynb as starting point).
- Build and harden privacy-preserving services and cryptographic primitives into secured APIs, leveraging the PSI implementation in server_logic.py and client_logic.py.
- Refactor notebooks into reproducible scripts and add CI pipelines, pinned environments and deterministic seeds to improve reproducibility.
- Harden FastAPI endpoints (input validation, avoid pickle for untrusted data, better error handling) and add unit/integration tests for cryptographic flows.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Junior Backend Developer
Confidence: Medium Generalist
Python developer building small, single-process educational and computational tools with a working Streamlit UI and test harness. The strongest proven skill is delivering an interactive mathematical/educational application with concrete UI flows and persistence calls, evidenced by my_galios/app.py which wires inputs, validation, batching and calls into core persistence functions. The codebase lacks evidence of production-grade backend concerns such as API design, migrations/transaction handling, auth, and observability integration.
API Design
1/10
How well APIs are designed
No REST/GraphQL API design visible; single-process Streamlit UI handlers only and no API-versioning, idempotency or error-contract conventions.
Evidence
my_galios/app.py: Streamlit-based UI handlers and button-driven flows; no HTTP API endpoints, versioning or idempotency keys
Data Layer & Database
2/10
Working with databases
Calls into a simple local persistence layer are present but there is no visible migration chain, transaction boundary handling, or tuned SQL; data operations appear basic.
Evidence
my_galios/app.py: calls initialize_database(), save_polynomials_to_db(), get_saved_polynomials() which indicate local persistence usage
Scalability & Performance
2/10
Handling load and speed
Some batching/pagination and generator state are used for computational tasks, but there is no evidence of connection pooling, caching strategy, queue decoupling or load testing artifacts.
Evidence
my_galios/app.py: BATCH_SIZE and use of find_irreducible_polynomials_batch with offset and generator_initialized flags
System Architecture
2/10
Overall system structure
Monolithic single-process Streamlit application structure; clear separation of UI and core functions exists but no multi-service decomposition, graceful degradation, or secret/config management patterns are visible.
Evidence
my_galios/app.py: main_galois orchestrates UI modes and delegates computation to core module functions (GaloisFieldExtension, find_irreducible_polynomials_batch)
Security & Auth
1/10
Protecting data and access
Minimal input validation is present (basic numeric and parse checks) but no authn/authz, input sanitization against injection, or secrets handling patterns are visible.
Evidence
my_galios/app.py: isprime checks and try/except around parsing user inputs; no authentication or authorization code
Reliability & Observability
2/10
Stability and monitoring
Basic application-level error handling and local logging into session state plus unit tests exist, but there is no evidence of structured tracing, timeouts with backoff, circuit breakers or production observability configuration.
Evidence
my_galios/app.py: operation_log stored in st.session_state and surfaced via download_button
my_galios/tests.py: pytest-based tests present (test harness exists)
Expertise
Python• Junior
Industries
Education• Middle
Technologies
SQLite
Pydantic• mentioned only
Recommendations
- Develop small to medium web-backed data apps or educational computation tools where Streamlit and Python are sufficient.
- Harden the data layer by adding explicit migrations, transactional boundaries, and tests around DB operations (move core/db.py to a visible, exercised module with migration history).
- Expand test coverage and CI for backend logic, and add structured logging and basic observability (request ids, error metrics) before attempting production deployments.
- If scaling is required, refactor long-running computation into worker processes or background jobs and surface progress via a stable API or message queue rather than blocking UI threads.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: