Potapova Anna
ML Engineer
4+ years exp
C++
SQL
C
Python
Java
Scalability & Performance: 5/10
System Architecture: 5/10
API Design: 4/10
Active 18 days ago
+7 (950) 5725269 Invite to interview
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Overview
Technical skills
Timeline
Roles
Overview
A backend-focused engineer with strong hands-on experience implementing distributed-consensus components and a second skillset in Java-based multiplayer game code; the Raft roles, transport layer and custom storage provide the strongest proof. The clearest proven skill is building a Raft implementation with concurrent replication and HTTP-based RPC, evidenced by core role implementations (leader/follower/candidate), transport sender/receiver and storage (LockStorage). The public code does not show production-grade API governance, authentication, metrics/observability rigs, or integration with durable databases and migration histories.
Technical skills
C++
SQL
C
Python• Middle • 4y+
Java• Middle • 3y+
C++
PyTorch C++
Python
Poetry
Uvicorn
Requests
FastAPI• 3y+
Databases
ElasticSearch
PostgreSQL
Qdrant
MongoDB
DevOps
Grafana
Kubernetes
Rest API
Docker• 3y+
Git
AI/ML
Claude
Claude Code
CLIP
ComfyUI
Computer Vision
Label Studio
LangChain
LangGraph
LLM
MLFlow
NLP
PyTorch
Reinforcement Learning
Scikit-learn
VLM
ChatGPT
Stable Diffusion
CatBoost
Dagster
DVC
LightGBM
RAG
YOLO
Robotics
Digital Twin
Timeline
Software Engineer / ML Engineer
•
Middle
True Engineering
•
Full-Time
Worked on ML and CV tasks across STT and speech interfaces in R&D, then moved to the ML team. Led and delivered multiple parallel ML projects in production, including object detection and document/image processing pipelines. Participated in presales estimates and architecture planning as part of partial team-lead responsibilities, and implemented agent-related work using agent frameworks. Built reusable internal ML components and contributed to model deployment workflows.
YOLO
Qdrant
Dagster
CatBoost
LightGBM
DVC
RAG
Seminar Lecturer
•
Middle
Novosibirsk State University
•
Part-Time
Taught the course on developing a software-hardware complex and supported student teams through the semester. Coordinated multiple teams, acting as both a PM and an ML-domain contributor. Guided project work including an RAG-based bot integrated into the university admissions workflow.
Novosibirsk State University
Bachelor's Degree •
Computer Science and Systems Engineering
Engineer-Programmer
•
Middle
Institute of Automation and Electromeasurements of the Siberian Branch of the Russian Academy of Sciences
•
Full-Time
Designed a modular system architecture for measuring and testing detector characteristics before sales. Implemented a FastAPI-based server for storing test results and configured deployment tooling using Docker and Python environment tooling. Coordinated a small development team and drove implementation of the system’s extensible test-set approach.
FastAPI
Docker
Middle Backend Developer
Confidence: Medium Distributed Systems
A backend-focused engineer with strong hands-on experience implementing distributed-consensus components and a second skillset in Java-based multiplayer game code; the Raft roles, transport layer and custom storage provide the strongest proof. The clearest proven skill is building a Raft implementation with concurrent replication and HTTP-based RPC, evidenced by core role implementations (leader/follower/candidate), transport sender/receiver and storage (LockStorage). The public code does not show production-grade API governance, authentication, metrics/observability rigs, or integration with durable databases and migration histories.
API Design
4/10
How well APIs are designed
Practical HTTP API endpoints and client/server RPC for Raft are implemented (FastAPI + HTTP sender/receiver) with basic error handling and status codes, but there is no explicit versioning strategy, idempotency key handling, or formal API contract beyond ad-hoc headers (TTL/Version).
Evidence
BuDum-Tss/PyRaft/pyraft/transport/receiver.py: FastAPI receiver endpoints (append_records, request_vote, set_value)
BuDum-Tss/PyRaft/pyraft/transport/sender.py: HttpSender methods append_records/request_vote/set_value with timeouts and response handling
BuDum-Tss/PyRaft/lock.py:_compare_and_swap - HTTP-based compare-and-swap using TTL header and Version response
Data Layer & Database
4/10
Working with databases
Custom in-memory SyncStorage and a LockStorage show awareness of versioning, TTL and compare-and-swap semantics; there is no evidence of schema migrations, durable DB integrations, or careful isolation-level management.
Evidence
BuDum-Tss/PyRaft/lock.py:LockStorage implements get_value/get_version/update with TTL timers and version bookkeeping
BuDum-Tss/PyRaft/pyraft/core/roles/leader.py:set_value appends records to the Raft log and consults sync_storage for value/version
BuDum-Tss/PyRaft/pyraft/transport/sender.py:set_value returns Version header and HTTP status mapping
Scalability & Performance
5/10
Handling load and speed
Concurrency and scalability considerations are present - leader uses ThreadPoolExecutor to push log entries in parallel and roles use thread/events - but there are no load-testing artifacts, cache strategies, or sophisticated backpressure/rate-limiting patterns.
Evidence
BuDum-Tss/PyRaft/pyraft/core/roles/leader.py:send_changes and use of ThreadPoolExecutor for parallel replication
BuDum-Tss/PyRaft/pyraft/core/roles/candidate.py:_request_votes uses ThreadPoolExecutor to parallelize RPCs
BuDum-Tss/PyRaft/pyraft/transport/sender.py:uses requests with explicit timeouts and logs connection failures
System Architecture
5/10
Overall system structure
Reasonable modular decomposition for a Raft implementation (core roles, transport, node, storage, api) and a separate Java module for a multiplayer game show purposeful boundaries; the architecture is medium-complex and pragmatic rather than opaquely microservice-y.
Evidence
BuDum-Tss/PyRaft/pyraft/core/*: clear separation of Role, Candidate/Follower/Leader implementations
BuDum-Tss/PyRaft/pyraft/transport/*: transport layer separated into sender/receiver
Foxtezy/DeadInByte/src/main/java/ru/nsu/fit/dib/projectdib/newMultiplayer/*: multiplayer context (EntitySpawner, ServerState, ClientState) separated from game UI/logic
Security & Auth
2/10
Protecting data and access
Minimal security controls - the code uses owner fields and TTL for CAS semantics but lacks authentication, authorization, and hardened input validation for network endpoints.
Reliability & Observability
4/10
Stability and monitoring
Observability and reliability basics exist - structured logging, timeouts and heartbeat/heartbeat-wait logic across Raft roles are implemented; retries/backoff, metrics, distributed tracing and jittered retries are not present.
Verified artifacts
Expertise
Python• Middle
Java• Middle
Industries
Gaming• Middle
Technologies
Python• Middle • 4y+
Java• Middle • 3y+
FastAPI• 3y+
Uvicorn
Requests
Recommendations
- Lead development of cluster coordination and consensus components (Raft-based features, leader election, replication) - implement and harden set_value/CAS flows.
- Build reliable networked services and real-time multiplayer backends - design the server-side networking (connection handling, authentication, rate limiting) for game/multiplayer use cases.
- Extend the current system with production-grade observability and security - add metrics, structured traces, auth, and retry/backoff policies for RPCs.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: