Keren Beryl
Software Tester
4+ years exp
Node JS
Python
Java
SQL
JavaScript
Data Pipeline & Feature Engineering: 7/10
Model Architecture & Training: 5/10
Experimentation & Evaluation: 5/10
Active 7 hours ago
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Overview
Technical skills
Timeline
Roles
Overview
Senior API-focused Python engineer specializing in building production-grade HTTP services. The strongest proven skill is designing a resilient URL-audit API surface and worker model, evidenced by app/main.py (lifespan/http client, semaphore, request-id middleware) and app/audit.py (timeout mapping, link-sampling, security header checks). There is little to no evidence of persistent databases, schema migrations, distributed state (Redis) or advanced auth/token management in the public code.
Technical skills
Node JS• Middle
Python• Senior • 4y+
Java• Junior
SQL• Middle
JavaScript• Junior
Python
HTTPX
Beautiful Soup
Pydantic
FastAPI
Uvicorn
Asyncio
Databases
MySQL
AI/ML
NumPy
SciPy
Speech Recognition
Ollama
Pandas
Scikit-learn
Streamlit
TensorFlow
Whisper
Frontend
npm
Frontend
DevOps
CI/CD
GitHub Actions
Rest API
Git
GitHub
Jenkins
Analytics
Tableau
Plotly
ETL/ELT
QA
JMeter
Playwright
Postman
Robot Framework
Selenium
Timeline
Anna University
Bachelor's Degree •
Artificial Intelligence and Data Science
Test Engineering Trainee
•
Junior
American Megatrends International (AMI)
•
Full-Time
Completed enterprise test engineering training with hands-on work using Selenium, Robot Framework, and JMeter. Developed page-object and data-driven automation frameworks and performed API, load, and stress testing. Integrated JMeter with Jenkins and used Git for version control while applying STLC and Agile practices.
Selenium
Robot Framework
JMeter
Jenkins
Git
Rest API
Motivated B.Tech graduate in Artificial Intelligence and Data Science with hands-on experience in software testing, test automation, and performance testing. Skilled in Selenium, Robot Framework, JMeter, API testing, Jenkins, Git, and SQL, with knowledge of STLC, SDLC, and Agile methodologies. Passionate about ensuring software quality, identifying defects, and building reliable and efficient applications.
Python
TensorFlow
Scikit-learn
Whisper
Flask
Streamlit
Data Science Intern
•
Junior
Cognifyz Technologies
•
Internship
Cleaned and transformed real-world datasets through preprocessing and feature engineering. Built and evaluated regression models using Python and related data tools to improve prediction performance. Supported data preparation work aligned with ETL-style validation and transformation tasks.
Python
Pandas
Plotly
Senior AI/ML Engineer
Confidence: High ML Engineer
Audio-focused ML engineer (senior-level) who produces end-to-end speech data pipelines and production-grade web APIs with careful feature engineering as their standout strength. The strongest proven skill is domain-specific audio feature engineering and signal-processing pipelines, demonstrated by the detailed implementations in core/feature_extractor.py and the dataset builder in data_pipeline/step1_build_dataset.py. There is limited public evidence of distributed training, deep-learning model development at scale, experiment tracking, or hardened model-serving infrastructure such as Triton/BentoML or CI-driven model lifecycle automation.
Model Architecture & Training
5/10
How well models are designed and trained
Competent classical ML training and model-management code: clear training pipeline, cross-validation, model selection and calibration using scikit-learn, plus a prediction wrapper that loads and applies the saved model. No evidence of custom neural architectures, distributed training or advanced optimizer/scheduler design.
Evidence
Speech-Smoothing-and-Severity-Classification/Code/data_pipeline/step3_train.py: cross_val_score, RandomForestClassifier/GradientBoostingClassifier, CalibratedClassifierCV, SMOTE handling
Speech-Smoothing-and-Severity-Classification/Code/core/severity_classifier.py:_predict_ml (model loading, scaler transform, predict_proba usage)
Data Pipeline & Feature Engineering
7/10
How data is prepared for models
Strong, domain-aware data pipeline and feature engineering for audio: end-to-end dataset builder, preprocessing, YouTube ingestion, a browser labeling UI, and a substantial custom audio feature extractor extracting MFCCs, prosodic, spectral and voice-quality features.
Evidence
Speech-Smoothing-and-Severity-Classification/Code/data_pipeline/step1_build_dataset.py: student/UASpeech/YouTube ingestion and preprocessing
Speech-Smoothing-and-Severity-Classification/Code/core/feature_extractor.py: MFCC, prosodic, spectral, energy and voice-quality feature extraction implementations
Speech-Smoothing-and-Severity-Classification/Code/data_pipeline/step2_label.py: browser-based labeling UI and export for training
Experimentation & Evaluation
5/10
How results are measured and tested
Reasonable experimentation and evaluation practices for classical ML: cross-validation, leave-one-out fallback, training-time classification reports, feature importance prints and basic class-balance handling. Lacks experiment tracking (W&B/MLflow), reproducible run manifests, or systematic hyperparameter search artifacts.
Evidence
Speech-Smoothing-and-Severity-Classification/Code/data_pipeline/step3_train.py: cross_val_score usage, classification_report, feature importance printing
Speech-Smoothing-and-Severity-Classification/Code/data_pipeline/step3_train.py: CV strategy selection (LOOCV vs StratifiedKFold) and CV-score reporting
MLOps & Deployment
4/10
How models are shipped to production
Practical service deployment and light MLOps: production-grade FastAPI service with async HTTP client, request-id middleware, rate-limiting middleware, tests, and model serialization to a bundle. No evidence of robust model serving (Triton/BentoML/KServe), CI/CD for model changes, model versioning, or monitoring/drift detection.
Evidence
Production-grade-Page-Pulse/app/main.py: FastAPI app, async lifespan with httpx.AsyncClient, request-id middleware, semaphore for concurrency
Speech-Smoothing-and-Severity-Classification/Code/core/severity_classifier.py: loads pickled model bundle saved by training script
Production-grade-Page-Pulse/tests/test_api.py: ASGITransport-based tests and rate-limit tests
Computational Efficiency
3/10
How efficiently computing resources are used
Some attention to efficiency: async non-blocking HTTP calls, concurrency control via semaphores, and n_jobs=-1 for RandomForest training. No evidence of GPU usage, quantization, memory profiling, or advanced inference optimization.
Evidence
Production-grade-Page-Pulse/app/main.py: audit_semaphore asyncio.Semaphore(MAX_CONCURRENT_AUDITS) and httpx.AsyncClient usage
Speech-Smoothing-and-Severity-Classification/Code/data_pipeline/step3_train.py: RandomForestClassifier(..., n_jobs=-1)
Research Depth & Innovation
5/10
Depth of research and new ideas
Solid applied research depth in audio signal processing and domain heuristics: custom jitter/shimmer, prosodic analysis, nPVI, speech-rate and pause detection with clinically informed thresholds. This is practical innovation in applied feature engineering rather than new academic algorithms.
Evidence
Speech-Smoothing-and-Severity-Classification/Code/core/feature_extractor.py: implementations for pitch (librosa.pyin), jitter/shimmer estimation, pause detection and speech-rate estimation
Speech-Smoothing-and-Severity-Classification/Code/core/severity_classifier.py: clinically-informed scoring heuristics and flags
Expertise
Audio & Speech Processing• Senior
MLOps & Model Lifecycle• Middle
Industries
Health Care• Middle
Education• Middle
Technologies
Scikit-learn
SciPy
Speech Recognition
Pandas
NumPy
Whisper
Deep Learning• mentioned only
Recommendations
- Build audio analysis and clinical-assessment tools that rely on handcrafted features and classical ML models, including labeling UIs and data ingestion pipelines.
- Implement production ML APIs and lightweight model-serving endpoints where pickled model bundles and Flask/FastAPI are acceptable.
- Improve MLOps by adding model versioning, reproducible experiment tracking (W&B/MLflow), and safer model serialization or containerized serving for production deployments.
- Extend audio feature work into hybrid systems by adding deep-learning front-ends (e.g., small CNN/RNN or pretrained embeddings) and documenting reproducible training runs
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Senior Backend Developer
Confidence: High API Engineer
Senior API-focused Python engineer specializing in building production-grade HTTP services. The strongest proven skill is designing a resilient URL-audit API surface and worker model, evidenced by app/main.py (lifespan/http client, semaphore, request-id middleware) and app/audit.py (timeout mapping, link-sampling, security header checks). There is little to no evidence of persistent databases, schema migrations, distributed state (Redis) or advanced auth/token management in the public code.
API Design
6/10
How well APIs are designed
API design shows deliberate choices: versioned route, Pydantic validation, consistent error payloads and request-id propagation; lacks advanced API patterns like idempotency key handling or extensive pagination strategies.
Evidence
Production-grade-Page-Pulse/app/main.py: POST /api/v1/audit endpoint, request_id_middleware and exception handlers
Production-grade-Page-Pulse/app/models.py: Pydantic AuditRequest and AuditResponse models
Production-grade-Page-Pulse/tests/test_api.py: tests asserting validation errors and presence of X-Request-ID
Data Layer & Database
2/10
Working with databases
No database layer or migration history is present; data integrity is enforced at the API boundary via Pydantic and there is an in-process TTL cache but no persistent storage or schema evolution artifacts.
Evidence
Production-grade-Page-Pulse/app/cache.py: TTLCache implementation
Production-grade-Page-Pulse/app/models.py: Pydantic models used for request/response validation
Scalability & Performance
6/10
Handling load and speed
Performance and scalability considerations are explicit: async httpx client, concurrency limiting via an asyncio.Semaphore, in-process TTL caching and a sliding-window in-memory rate limiter; the design stops short of distributed/cluster-level controls (Redis, shared rate-limit store, circuit breakers).
Evidence
Production-grade-Page-Pulse/app/main.py: audit_semaphore and httpx.AsyncClient lifecycle usage
Production-grade-Page-Pulse/app/rate_limit.py: RateLimitMiddleware (sliding-window, per-client in-memory limiter)
Production-grade-Page-Pulse/app/cache.py: TTLCache used to short-circuit repeated audits
System Architecture
6/10
Overall system structure
Reasonable modular decomposition with clear separation between HTTP surface, audit logic, caching and rate limiting, plus test coverage; architecture is designed for production single-process deployment but not yet extended to multi-instance coordination.
Evidence
Production-grade-Page-Pulse/app/main.py: application composition, middleware registration and lifespan management
Production-grade-Page-Pulse/app/audit.py: encapsulated audit logic separated from request handling
Security & Auth
5/10
Protecting data and access
Security-aware at the input boundary with Pydantic validation, URL scheme checks, and security header analysis, plus rate limiting; missing stronger protections such as SSRF whitelisting, secrets handling, authentication flows or token lifecycle management.
Evidence
Production-grade-Page-Pulse/app/audit.py: scheme validation (http/https) and security header analysis in _analyze_security_headers
Production-grade-Page-Pulse/app/models.py: Pydantic validation for AuditRequest
Production-grade-Page-Pulse/app/rate_limit.py: client identification using X-API-Key or remote IP
Reliability & Observability
6/10
Stability and monitoring
Good reliability and observability basics: request IDs, structured logging configuration, explicit timeout handling and mapped errors, semaphore-based concurrency limits and tests for error paths; lacks distributed tracing, backoff/retry policies, and external monitoring integrations shown in code.
Evidence
Production-grade-Page-Pulse/app/main.py: request_id_middleware and global exception handlers
Production-grade-Page-Pulse/app/logging_config.py: JSONFormatter and configure_logging
Production-grade-Page-Pulse/app/audit.py: _fetch maps httpx timeouts and raises AuditError, and tests/test_audit.py covers timeout and error cases
Expertise
Python• Middle
Microservices & API Architecture• Senior
Technologies
Python• Senior • 4y+
FastAPI
Beautiful Soup
Asyncio
Pydantic
HTTPX
Uvicorn
Recommendations
- Lead development of HTTP APIs and lightweight audit/monitoring services, especially where async IO and concurrency limits are required.
- Implement horizontally scalable features such as Redis-backed rate limiting, shared cache invalidation and distributed tracing integration (e.g., OpenTelemetry).
- Build more resilient link-checking/background jobs with exponential backoff, retry policies and circuit-breaker patterns and expand integration tests to cover multi-instance behavior.
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
Middle-level app engineer specializing in compact, well-tested client-side single-page interactions and shareable, browser-only state flows. The strongest proven skill is building a small, robust browser-only SPA with URL-safe state encoding and an accompanying automated e2e test harness, as shown by best-friend-quiz/js/app.js, best-friend-quiz/js/codec.js, and best-friend-quiz/test/e2e.js. There is little public evidence of large-scale backend production services, advanced performance engineering, or use of major frontend frameworks in larger systems.
UI Component Architecture
4/10
How interface parts are built
Clear, hand-rolled screen/state separation and small DOM-centric components; pragmatic but not componentized into reusable UI modules or a design-system library.
Evidence
best-friend-quiz/js/app.js: screens object + showScreen() implementing the app-level state machine and screen switching
best-friend-quiz/index.html: semantic screen sections (#screen-home, #screen-name, #screen-quiz, #screen-share, #screen-reveal) used as UI boundaries
Responsive & Cross-browser
5/10
Works on all screens and browsers
Responsive CSS and pragmatic cross-environment guards; explicit reduced-motion handling and fallbacks for APIs that may be absent in some hosts.
Evidence
best-friend-quiz/css/style.css: media queries and responsive rules (max-width, prefers-reduced-motion)
Keren-beryl/keren-beryl.github.io/index.html: JS guards around focus() and scrollTo() and matchMedia('(prefers-reduced-motion') usage
Performance Optimization
3/10
Speed of the interface
Some measured-friendly choices (rAF for animations, reduced-motion), but no advanced bundle or runtime optimization, code-splitting, or virtualization.
Evidence
Keren-beryl/keren-beryl.github.io/index.html: requestAnimationFrame-driven hero animation with prefers-reduced-motion checks
best-friend-quiz/js/app.js: animation and UI update loops using rAF and small, efficient DOM updates
Accessibility & Semantics
6/10
Usable for everyone
Takes accessibility into account: focus management (guarded), keyboard handling, ARIA roles/labels, and visible focus styles are present and intentionally used.
Evidence
best-friend-quiz/js/app.js: showScreen() sets tabindex and guards focus(); option buttons set aria-pressed and keyboard Enter handling for inputs
best-friend-quiz/index.html: #toast role=status and aria-live='polite'; semantic structure and aria-friendly patterns
State Management & Data Flow
4/10
Managing data in the app
A small but deliberate client-side state machine with safe encoding of application state into URL-safe tokens; good defensive decoding and boot-time hash handling, but no complex server-state discipline or cancellation logic.
Evidence
best-friend-quiz/js/app.js: state object, boot() handling of hash params, startQuiz/finishQuiz flows
best-friend-quiz/js/codec.js: Codec.encodeJoin / encodeResult and decode() which returns null on malformed input instead of throwing
UX & Visual Polish
5/10
Look and feel quality
Strong UX polish for a small app: animated micro-interactions, progress indicators, toast messaging and graceful error UX; good perceived-performance and presentation work for the product scope.
Evidence
best-friend-quiz/css/style.css: progress bar, animated reveal, transitions, and well-styled interactive states
best-friend-quiz/index.html + js/app.js: toast messages, progress-dots and reveal UI with animated fill and tier text
Expertise
Frontend Architecture & Build Tools• Middle
Technologies
Frontend
CI/CD
Node JS• Middle
GitHub Actions
npm
GitHub
Recommendations
- Build and maintain small-to-medium single-page interactive experiences and static-hosted products that need client-only state (shareable links, encoded state).
- Develop polished, accessible UI widgets and flows where keyboard/focus and reduced-motion are important, improving the app-level UX patterns used in this work.
- Write and own testable prototypes and demo applications with robust e2e tests and clear boot/rehydration behavior for stakeholder demos and user testing.
- Iterate on frontend architecture and build tooling (CI/CD pipelines, npm scripts, and GitHub Actions) for small teams shipping static frontends.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: