Sergey Sysoev
ML Engineer
4+ years exp
C
C++
Python
Data Pipeline & Feature Engineering: 4/10
Experimentation & Evaluation: 4/10
Active 19 days ago
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Overview
Technical skills
Timeline
Roles
Overview
A pragmatic embedded/firmware engineer at a middle level who builds ESP32-based device firmware and companion Python tools. The strongest proven skill is implementing device configuration and control stacks (AsyncWebServer/Preferences + FreeRTOS task pattern) as evidenced by InitializeSettingsServer.cpp and WiFiConfigManager.cpp. The work lacks register-level drivers, formal real-time analysis, watchdog/power management and on-device HIL/OTA testing evidence.
Technical skills
C
C++• Middle • 4y+
Python• Middle • 4y+
C
FFmpeg• 3y+
C++
PyTorch C++
CMake
STL
Python
Asyncio• 3y+
Databases
PostgreSQL• 3y+
AI/ML
AI Agents
Computer Vision
RAG
Speech Recognition
Ultralytics
NumPy
Pandas
Scikit-learn
Numba
SciPy
Jupyter Notebook
llama.cpp• 3y+
LLM• 3y+
Ollama• 3y+
ONNX• 3y+
OpenCV• 3y+
PyTorch• 3y+
TensorRT• 3y+
Transformers• 3y+
vLLM• 3y+
Whisper• 3y+
DevOps
Docker• 3y+
Git• 3y+
IoT
FreeRTOS
IoT
Timeline
Computer Vision / Applied ML Engineer
•
Middle
SberBank
•
Full-Time
Built and adapted transformer-based models for production use with strict latency and resource constraints. Developed OCR and speech/ASR pipelines end-to-end, including model adaptation, post-processing, validation, and quality control, and deployed optimized edge inference on ARM/Linux. Improved inference performance via ONNX/TensorRT optimization and integrated ML inference stacks (Ollama, llama.cpp, vLLM) into streaming and agent-focused tooling for production systems.
Python
PyTorch
Transformers
Whisper
ONNX
TensorRT
OpenCV
Docker
Asyncio
PostgreSQL
Git
C++
FFmpeg
Ollama
llama.cpp
vLLM
LLM
A streaming pipeline framework for pre-annotating object-detection datasets.
Point it at a stream of images, wire up a graph of nodes — dedup, detect, embed, select, save — and get a pre-labelled dataset ready for human review.
Python
YOLO
OpenCV
agent dashboard
AI/ML
Веду проект - аналог Jira с агентами. Используем опенсорсные и проприетарные harness для интеграции с dashboards + локализованная среда для исполнения задач агентами(изолированный докер контейнер в котором агент не может сильно навредить в случае допущения ошибки). Функционала сильно больше, но в двух словах так.
Python
FastAPI
LangGraph
Docker Compose
Ruff
pre-commit
vLLM
Ollama
National Research Nuclear University
Master's Degree •
информационные системы и технологии
National Research Nuclear University
Bachelor's Degree •
Computer science
Middle AI/ML Engineer
Confidence: High Research
A developer focused on classical computer vision research and small tooling, operating at a solid junior-to-middle level. The strongest proven skill is designing and running reproducible evaluation experiments for blur-detection metrics, evidenced by the notebook that extracts multiple handcrafted features, benchmarks implementations, and trains a KNeighbors classifier (research.ipynb) together with evaluation helpers (validate_methods.py and time_checker.py). There is limited evidence of production ML engineering, serving, distributed training, or custom model architectures in public code.
Model Architecture & Training
2/10
How well models are designed and trained
Basic model usage and evaluation - uses scikit-learn KNN and standard preprocessing but no custom architectures, loss design, or training pipelines.
Data Pipeline & Feature Engineering
4/10
How data is prepared for models
Clear data preparation and feature engineering for classical CV metrics - reads image sets, extracts multiple handcrafted metrics, standardizes features and splits train/test.
Experimentation & Evaluation
4/10
How results are measured and tested
Solid experiment and evaluation work for a small research project - classification reports, confusion matrices, plotting and runtime benchmarking are present.
MLOps & Deployment
1/10
How models are shipped to production
Minimal MLOps or deployment artifacts - small CLI application and basic runtime guards but no serving, versioning, or model lifecycle automation.
Computational Efficiency
3/10
How efficiently computing resources are used
Some attention to computational efficiency - uses numba and explicit timing to measure hotspots, but no measured before/after GPU/CPU profiling or production optimizations.
Research Depth & Innovation
3/10
Depth of research and new ideas
Research-oriented work at a modest depth - thoughtful comparison of classical metrics and an effective feature-combination idea, but no novel algorithmic contribution or paper-grade reproduction.
Expertise
Computer Vision & Image Analysis• Middle
Technologies
Python• Middle • 4y+
PostgreSQL• 3y+
llama.cpp• 3y+
OpenCV• 3y+
vLLM• 3y+
Jupyter Notebook
Numba
Scikit-learn
SciPy
Computer Vision
AI Agents
Speech Recognition
ONNX• 3y+
TensorRT• 3y+
Ollama• 3y+
Transformers• 3y+
Pandas
NumPy
Git• 3y+
PyTorch• 3y+
Docker• 3y+
LLM• 3y+
RAG
Asyncio• 3y+
Whisper• 3y+
PyTorch C++
Ultralytics
Recommendations
- Develop CPU-first CV pipelines and benchmarking suites that produce reproducible timing and accuracy reports, extending the existing time_checker and validation harness.
- Harden and unit-test the metric implementations and evaluation scripts, adding CI tests and small synthetic-unit tests for edge cases and corrupted inputs.
- Convert the exploratory notebook steps into reusable Python modules and a small experiment runner that logs runs and parameters (e.g., simple CSV/JSON logging or lightweight MLflow/W&B integration).
- Improve robustness of data handling - remove hard-coded paths, add argument parsing or config files, and add defensive checks around file I/O and image loading.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Game Developer
Confidence: Medium Generalist
A pragmatic generalist developer at a Middle level (tier 3.0) who writes both small C++/SDL graphics prototypes and TypeScript service tooling. The strongest proven skill is building reliable Node.js integrations and service tooling with Google APIs and an MCP server, evidenced by src/index.ts, src/auth.ts, src/gmail.ts and package.json. There is limited evidence of advanced game-engine systems, renderer/shader engineering, deterministic simulation or measured performance optimization, and some C++ prototype code contains correctness issues rather than hardened production patterns.
Gameplay Systems & Mechanics
2/10
How game logic works
Basic gameplay loop and input-driven prototype code is present (SDL event loop, simple state like paused), but there are no non-trivial gameplay systems, decoupled state machines, or save/version migrations.
Graphics & Rendering
3/10
Drawing game visuals
Author wrote a small software renderer-style demo with explicit 3D rotation and projection math driving SDL rendering; this is useful graphics prototype work but not advanced renderer or shader engineering.
Physics & Math
3/10
Game physics and math
Clear understanding of basic 3D/mathematical transforms and coordinate conversions (polar/cartesian), but no evidence of spatial partitioning, deterministic simulation, or custom physics integrators.
Engine Proficiency
3/10
Skill with the game engine
Comfortable with engine/tooling basics - CMake packaging and SDL integration are authored, and the TypeScript project shows deliberate packaging and script usage - but there is not extensive evidence of editor tooling or deep engine customizations.
Performance & Frame Budget
1/10
Keeping the game smooth
Very little demonstrated performance engineering or measured frame-budget work; render loop uses VSync and reasonable framerate-friendly patterns but no profiling, pooling, or zero-alloc hot-paths are present.
Content Pipeline & Tooling
1/10
Tools for game content
Minimal content-pipeline and tooling evidence beyond standard build scripts (CMake, npm). No asset import automation, CI pipeline configs, or editor tool plugins were found.
Expertise
Gameplay & Mechanics Development• Junior
Industries
Internet Services• Middle
Technologies
C++• Middle • 4y+
CMake
STL
Recommendations
- Own small-to-medium backend integrations and tooling (OAuth flows, API wrappers, service CLI) in Node.js/TypeScript.
- Develop and prototype gameplay mechanics or interactive tech demos where a compact C++/SDL codebase suffices.
- Implement build and deployment automation (CMake improvements, CI for native builds and TypeScript packaging) and harden cross-platform toolchains.
- Avoid putting large rendering/engine tasks or high-performance netcode on this developer alone until deeper profiling and deterministic-simulation experience is proven.
Middle Embedded Engineer
Confidence: Medium Firmware Engineer
A pragmatic embedded/firmware engineer at a middle level who builds ESP32-based device firmware and companion Python tools. The strongest proven skill is implementing device configuration and control stacks (AsyncWebServer/Preferences + FreeRTOS task pattern) as evidenced by InitializeSettingsServer.cpp and WiFiConfigManager.cpp. The work lacks register-level drivers, formal real-time analysis, watchdog/power management and on-device HIL/OTA testing evidence.
Embedded & Firmware
4/10
Low-level device code
Embedded firmware work on ESP32 with FreeRTOS task usage, web configuration portal, Preferences-based persistent settings and application-level servo control. The code shows practical RTOS task creation and lifecycle handling but relies on Arduino/AsyncWebServer/Servo libraries and lacks register-level drivers, ISR design, watchdog strategy or low-power measurements.
Evidence
Bleaff/ArduinoViolations/InitializeSettingsServer.cpp: start_monitor_thread (xTaskCreatePinnedToCore) and monitor_network_connection
Bleaff/servo_server/WiFiConfigManager.cpp: connectSavedWiFi (WiFi.begin with timeout loop) and startConfigPortal
Bleaff/servo_server/ServoController.cpp: begin/stopServos/attachServer (servo.attach + web handlers writing microseconds)
Hardware-Software Interface
3/10
Connecting code to hardware
Hardware-software interface is implemented at the protocol/API level (HTTP endpoints for servo and camera control, framed TCP stream reader) rather than register-level drivers. There is correct framing of streamed JPEG frames and simple request/response control for servos, but no register/datasheet-referenced peripheral drivers or bus recovery logic.
Evidence
Bleaff/servo_server/servoCamera.py: stream_receiver (4-byte size prefix framing and recv loop)
Bleaff/servo_server/ServoController.cpp: HTTP handlers /move /stop /status (microseconds writes via Servo library)
Bleaff/servo_server/WiFiConfigManager.cpp: setupServerRoutes (HTTP /save route storing servo pin settings into Preferences)
Resource Constraints
2/10
Working with limited resources
Some resource-awareness (Preferences for persistent storage, tuning stack size when creating a FreeRTOS task) but no linker scripts, flash/RAM budgeting, or measured power / memory footprints. Static defaults and simple persisted values are used but there is limited evidence of rigorous resource-constrained design.
Real-time & Timing
3/10
Precise timing control
Basic timing and latency awareness is present (WiFi connect timeout loops, frame-rate and bitrate computation in stream reader, microsecond servo control). However the firmware uses blocking delays and polling loops rather than capture/compare timers or documented WCET reasoning, and no priority inversion mitigation is visible.
Evidence
Bleaff/servo_server/servoCamera.py: FPS measurement and recv loop timing in stream_receiver
Bleaff/ArduinoViolations/InitializeSettingsServer.cpp: monitor_network_connection with connect timeout logic
Bleaff/servo_server/ServoController.cpp: microsecond writes via writeMicroseconds for servo positioning
HDL & Circuit Logic
Designing digital circuits
Not evidenced in public code
Reliability & On-device Testing
2/10
Testing on real hardware
Some pragmatic reliability steps - persistent portal option, reconnect attempts, reboot on settings save - but no watchdog strategy, firmware CRC/versioning, OTA rollback, or on-device automated tests / HIL evidence. Error handling is basic and there are blocking calls that could reduce robustness in constrained scenarios.
Evidence
Bleaff/servo_server/WiFiConfigManager.cpp: /save handler triggers ESP.restart() after storing settings
Bleaff/servo_server/WiFiConfigManager.cpp: connectSavedWiFi includes a 10s timeout loop with delay and status checks
Bleaff/ArduinoViolations/InitializeSettingsServer.cpp: monitor_network_connection implements connect retry and prints connection status
Expertise
Firmware• Middle
IoT & Connectivity Protocols• Middle
Industries
Hardware• Middle
Technologies
IoT
FreeRTOS
Recommendations
- Implement and maintain ESP32-based IoT device firmware (web configuration portals, persistent settings, network lifecycle).
- Develop companion host-side tooling for camera/servo systems (robust TCP framed streams, thread-safe buffering and UI display).
- Extend firmware towards production-readiness: add watchdogs, OTA with rollback, non-blocking drivers and resource budgeting.
- Integrate automated on-device tests or HIL (Renode/QEMU or hardware testbenches) and add firmware versioning and CRC checks.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: