Namira Ganam
AI Operations Specialist
4+ years exp
Management: Under a year
Python
JavaScript
C#
Node JS
C++
SQL
C
Data Pipeline & Feature Engineering: 4/10
Active 2 days ago
Invite to interview
Download CVCV
Overview
Technical skills
Timeline
Roles
Overview
A pragmatic ML practitioner (middle level) who builds end-to-end predictive prototypes and dashboards, particularly for healthcare and cloud-cost analysis. The strongest proven skill is applied supervised modeling and evaluation as demonstrated by the fetal_health and medical_prediction notebooks that include feature engineering, GridSearchCV, cross-validation and model export. What is not evidenced is production-grade MLOps, rigorous uncertainty quantification, automated pipelines, or test/CI infrastructure.
Phone
Technical skills
Languages
7
Python
JavaScript
C#
Node JS
C++
SQL
C
Python
5
Flask
Requests
FastAPI
SQLAlchemy
Pydantic
DevOps
5
Git
Linux
AWS
Amazon EC2
GitHub
AI/ML
11
RAG
Pandas
Scikit-learn
Transformers
LLM
Streamlit
OpenAI
Hugging Face
NumPy
PyTorch
TensorFlow
Other
23
.NET
Axios
MySQL
PyTorch C++
TensorFlow C++
Matplotlib
Power BI
MS SQL
Docker
Windows
Rest API
Jupyter Notebook
Tableau
Machine Learning
Deep Learning
Embeddings
Semantic Search
Computer Vision
NLP
Transfer Learning
Semantic Search
Sentiment Analysis
CNN
Timeline
George Brown College
Non-Degree Program •
Cloud Computing Technology
AI/ML Research Intern
•
Junior
Kinetics Solutions
•
Full-Time
Conducted AI/ML research focused on sustainability recommendations for energy and water-management use cases. Evaluated AI tools, libraries, and LLM-based approaches against cost, reliability, and long-term feasibility criteria. Synthesized multiple Canadian sources into board-ready findings on tight timelines and translated research into business- and market-aligned adoption recommendations.
LLM
Junior Full Stack Developer / Junior Data Scientist
•
Junior
Odysseus Solutions Pvt. Ltd.
•
Full-Time
Improved end-to-end ML pipeline performance by adapting Python-based preprocessing to changing product requirements, reducing data-cleaning turnaround time. Built production ML pipelines for a live cruise analytics, search, and booking platform and created an internal tool to refine trend-forecast predictions. Delivered full-stack features and client updates using C#, .NET, JavaScript, Flask, and REST APIs, developing on Linux and managing code via Git-based pull requests.
Python
C#
.NET
JavaScript
Flask
Linux
Git
Maharaja Sayajirao University of Baroda (MSU)
Master's Degree •
Computer Application
Middle AI/ML Engineer
Confidence: Medium ML Engineer
Applied ML engineer (middle level) focused on production-facing NLP and analytics pipelines with a strength in integrating pretrained NLP/LLM components into web services and data workflows. The strongest proven skill is building end-to-end inference features and evaluation flows, evidenced by the FastAPI backend and ai_service.py OpenAI integration plus the Streamlit sentiment UI and batch_sentiment.py evaluation pipeline. There is no evidence of custom model training loops, distributed training, experiment tracking, or advanced MLOps and security hardening in the public code.
Model Architecture & Training
3/10
How well models are designed and trained
Applies pretrained models and standard algorithms (transformer pipelines, IsolationForest) but no custom architectures, training loops, schedulers, or regularization experiments are present.
Evidence
transformer-sentiment-analysis/src/batch_sentiment.py: uses transformers.pipeline("sentiment-analysis") and computes metrics
namirag/aws-cloud-cost-analyzer/src/ml_anomaly_detection.py: trains sklearn IsolationForest on daily cost
Data Pipeline & Feature Engineering
4/10
How data is prepared for models
Concrete data engineering work is present: cleaning, validation, synthetic data generation and feature extraction (dates/months), but at a single-node script level without production ETL tooling.
Evidence
namirag/aws-cloud-cost-analyzer/src/clean_data.py: clean_billing_data handles dropna, type coercion, date fields and rounding
namirag/aws-cloud-cost-analyzer/src/generate_data.py: synthetic AWS billing dataset generation with seasonal and environment factors
namirag/aws-cloud-cost-analyzer/notebooks/01_cost_analysis.ipynb: preprocessing and grouping for monthly/service/region analysis
Experimentation & Evaluation
3/10
How results are measured and tested
Basic experimentation and evaluation pipelines exist (accuracy/precision/recall/F1, confusion matrix, CSV outputs) but there is no experiment tracking, reproducible runs, ablation, or hyperparameter search.
Evidence
transformer-sentiment-analysis/src/batch_sentiment.py: computes accuracy_score, precision_score, recall_score, f1_score
transformer-sentiment-analysis/pages/2_File_Analysis.py: displays confusion matrix and evaluation metrics in Streamlit
namirag/aws-cloud-cost-analyzer/notebooks/01_cost_analysis.ipynb: statistical anomaly thresholding and comparison with ML results
MLOps & Deployment
3/10
How models are shipped to production
Shows deployment-facing engineering: a FastAPI backend with DB models and a Streamlit UI, plus SQLAlchemy usage; lacks CI/CD manifests, containerization, monitoring, model versioning, or inference scaling logic.
Evidence
ai-resume-analyzer/backend/main.py: FastAPI endpoints for upload, retrieval and analyze-match
ai-resume-analyzer/backend/database.py: SQLAlchemy engine and SessionLocal setup
transformer-sentiment-analysis/app.py and pages/: Streamlit app UI and pages
Computational Efficiency
1/10
How efficiently computing resources are used
Minimal evidence of computational efficiency work; no explicit batching strategies, GPU tuning, quantization, memory profiling or distributed training present.
Evidence
transformer-sentiment-analysis/src/batch_sentiment.py: uses transformers.pipeline without custom batching or performance tuning
namirag/aws-cloud-cost-analyzer/src/ml_anomaly_detection.py: single-node sklearn IsolationForest usage
Research Depth & Innovation
1/10
Depth of research and new ideas
No signs of original research, custom layers, paper reproductions, or algorithmic innovation; the code integrates and applies existing libraries and models only.
Evidence
ai-resume-analyzer/backend/ai_service.py: uses hosted OpenAI Responses API with an application prompt rather than custom model research
transformer-sentiment-analysis/*: relies on Hugging Face pretrained pipelines rather than custom architecture implementations
Verified artifacts
Expertise
LLM• Junior
Document Intelligence & OCR• Junior
Industries
Data & Analytics• Middle
Technologies
SQL• since 2026 • Junior
C++• since 2026 • Junior
MySQL
Jupyter Notebook
Embeddings
Computer Vision• since 2026
NLP• since 2026
Transfer Learning
MS SQL
Transformers
TensorFlow• since 2026
NumPy
Git• since 2023
PyTorch• since 2026
AWS• since 2026
Docker
LLM• since 2025
RAG• since 2023
TensorFlow C++
PyTorch C++
Requests
Amazon EC2
Semantic Search
CNN• since 2026
Sentiment Analysis
OpenAI
GitHub
Semantic Search
Linux• since 2023
Windows
Machine Learning
NLP• mentioned only
Sentiment Analysis• mentioned only
Recommendations
- Develop production-ready NLP inference microservices with authentication, input validation, retries/backoff and logging based on the FastAPI ai_service.py pattern.
- Build reproducible experiment tracking (MLflow or W&B) and add deterministic training/eval scripts for any model tuning beyond pipeline usage.
- Harden upload and model inference endpoints with size/type checks, rate limits and sanitization before saving model outputs to the database.
- Extend data pipelines into simple ETL jobs (Airflow/Prefect) and add unit/integration tests and CI to make features production-safe.
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 pragmatic ML practitioner (middle level) who builds end-to-end predictive prototypes and dashboards, particularly for healthcare and cloud-cost analysis. The strongest proven skill is applied supervised modeling and evaluation as demonstrated by the fetal_health and medical_prediction notebooks that include feature engineering, GridSearchCV, cross-validation and model export. What is not evidenced is production-grade MLOps, rigorous uncertainty quantification, automated pipelines, or test/CI infrastructure.
Statistical Rigor
5/10
Correct use of statistics
Some solid statistical checks and evaluation are present (VIF, cross-validation, ROC-AUC, confusion matrices) but there is limited evidence of formal uncertainty quantification, hypothesis testing discipline, or careful treatment of multiple comparisons and causal assumptions.
Evidence
namirag/Pregnancy_risk_prediction/notebook/fetal_health_prediction.ipynb: variance_inflation_factor and VIF-based feature drop
namirag/Pregnancy_risk_prediction/notebook/medical_prediction.ipynb: GridSearchCV and cross_val_score usage with ROC-AUC calculation
namirag/aws-cloud-cost-analyzer/notebooks/01_cost_analysis.ipynb: statistical thresholding (mean + 3*std) and anomaly comparison to ML anomalies
Data Wrangling & Cleaning
6/10
Preparing and cleaning data
Data cleaning and provenance are explicit in scripts; there is a reusable cleaning function, numeric coercion, duplicate/missing-value handling, and a synthetic data generator used to create realistic billing data.
Evidence
namirag/aws-cloud-cost-analyzer/src/clean_data.py: clean_billing_data with date parsing, drop_duplicates, dropna, to_numeric and filtering
namirag/aws-cloud-cost-analyzer/src/generate_data.py: synthetic billing dataset generation with seeded randomness and anomaly injection
namirag/Pregnancy_risk_prediction/notebook/fetal_health_prediction.ipynb: missing value checks, inf-to-NaN handling and explicit outlier removal
Exploratory Analysis & Visualization
6/10
Exploring and visualizing data
Exploratory analysis is thorough and accompanied by multiple visualizations and written interpretations, though some plots are standard templates rather than targeted causal exploration.
Evidence
namirag/aws-cloud-cost-analyzer/notebooks/01_cost_analysis.ipynb: monthly/service/region plots and written summary recommendations
namirag/Pregnancy_risk_prediction/notebook/medical_prediction.ipynb: pairplots, heatmap, boxplots and descriptive narrative
namirag/Pregnancy_risk_prediction/codebase/dashboard_graphs.py: Plotly bubble and pie chart creation with explanatory text
Predictive Modeling
5/10
Building models that predict
Multiple predictive models, hyperparameter tuning, and evaluation metrics are used; deployment prototypes (Streamlit) exist, but production-grade concerns (pipelines, feature transforms, calibration, careful shape handling) are limited.
Evidence
namirag/Pregnancy_risk_prediction/notebook/fetal_health_prediction.ipynb: training multiple classifiers, GridSearchCV, model evaluation and feature importance
namirag/Pregnancy_risk_prediction/notebook/medical_prediction.ipynb: RandomForest and GradientBoosting training and export via pickle
namirag/transformer-sentiment-analysis/src/batch_sentiment.py: transformers pipeline usage and calculation of accuracy/precision/recall/f1
Business Insight & Impact
4/10
Turning analysis into business value
Some actionable insights are produced (AWS cost driver recommendations and maternal-health dashboard narratives), but linking model outcomes to explicit business metrics or cost/risk tradeoffs is limited.
Evidence
namirag/aws-cloud-cost-analyzer/notebooks/01_cost_analysis.ipynb: 'Recommended review areas' and identification of cost drivers
namirag/Pregnancy_risk_prediction/codebase/dashboard_graphs.py: dashboard narrative describing how visuals map to policy questions
namirag/Pregnancy_risk_prediction/app.py: Streamlit interface framing models for clinical usage and patient reports
Reproducibility & Notebook Hygiene
3/10
Clean, repeatable analysis
Some reproducibility signals exist (requirements.txt, occasional seeds, Streamlit caching) but notebooks contain absolute paths, ad-hoc pip calls, no CI/tests, no data or model versioning, and several hardcoded local paths.
Expertise
Data Science• Middle
Industries
Health Care• Middle
Technologies
Deep Learning
Scikit-learn
Pandas
Streamlit• since 2026
Hugging Face
Machine Learning• mentioned only
NLP• mentioned only
Sentiment Analysis• mentioned only
Recommendations
- Develop prototype ML models and Streamlit dashboards for quick delivery of analytic features and model-backed reports in healthcare or cost analytics.
- Implement model evaluation pipelines and ML engineering improvements: replace ad-hoc notebooks with reusable pipelines, add feature-transform pipelines, and add unit tests and CI for reproducibility.
- Build clean ETL modules and data validation around the cleaning scripts (expand clean_billing_data into a parameterized pipeline and add schema checks with tools like pandera or Great Expectations).
- Harden deployments by removing hardcoded paths and secrets, adding model versioning (DVC or MLFlow) and consistent serialization/loading of preprocessors and models.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Backend Developer
Confidence: High API Engineer
Backend-focused Python developer (Middle) building REST APIs and lightweight AI-powered services. The strongest proven skill is integrating LLM-based analysis into an API, evidenced by ai-resume-analyzer/backend/ai_service.py together with the FastAPI endpoints in ai-resume-analyzer/backend/main.py. There is little evidence of production-grade operations, migrations, authentication, observability or advanced scalability work in public code.
API Design
3/10
How well APIs are designed
Functional REST endpoints implemented in FastAPI and Flask with basic CORS and error responses, but missing API versioning, pagination, idempotency keys and consistent error contract.
Evidence
ai-resume-analyzer/backend/main.py: endpoints upload_resume, get_resume, create_job, analyze_resume_match
multi-tier-web-app/backend/app.py: routes products (GET) and add_product (POST)
Data Layer & Database
3/10
Working with databases
Uses SQLAlchemy ORM with declarative models and SessionLocal patterns and a simple raw mysql-connector usage, but no migration history, no explicit transaction boundaries or isolation awareness and no tuned queries/indexes.
Evidence
ai-resume-analyzer/backend/database.py: create_engine and SessionLocal
ai-resume-analyzer/backend/models.py: Resume, JobDescription, Analysis SQLAlchemy models
multi-tier-web-app/backend/app.py: mysql.connector usage with parameterized INSERT
Scalability & Performance
2/10
Handling load and speed
Little evidence of deliberate scalability or performance engineering; no caching/invalidation, no queuing, no rate limiting and no measured optimizations. The code relies on framework defaults (engine pooling) and contains a single async upload handler.
Evidence
ai-resume-analyzer/backend/main.py: async upload_resume (file ingestion)
ai-resume-analyzer/backend/database.py: create_engine (default pooling behavior only)
System Architecture
3/10
Overall system structure
Reasonable module separation for a small app (database, models, ai_service, main) and a clear frontend/backend split, but no evidence of inter-service contracts, graceful degradation strategies or service decomposition trade-offs for distributed operation.
Evidence
ai-resume-analyzer/backend/ai_service.py + ai-resume-analyzer/backend/main.py: separation of AI logic from API surface
multi-tier-web-app/backend/app.py and multi-tier-web-app/frontend/app.js: clear frontend-backend separation
Security & Auth
2/10
Protecting data and access
Some secrets are sourced from environment variables in the AI service and database modules, but critical security gaps exist including hardcoded DB credentials in a Flask app, no authentication/authorization, and only minimal input validation.
Evidence
multi-tier-web-app/backend/app.py: hardcoded RDS credentials (host, user, password, database, port)
ai-resume-analyzer/backend/ai_service.py: OpenAI client initialized with os.getenv('OPENAI_API_KEY') and ai-resume-analyzer/backend/database.py: load_dotenv and DATABASE_URL usage
Reliability & Observability
2/10
Stability and monitoring
Minimal reliability and observability practices: sessions are committed and closed but there are no retries/backoff, timeouts, structured logging, graceful shutdown handling or metrics/alerts integrated.
Evidence
ai-resume-analyzer/backend/main.py: DB session add/commit/close patterns without retry or timeout handling
ai-resume-analyzer/backend/ai_service.py: single OpenAI API call (client.responses.parse) with no error handling or timeouts
Expertise
Backend AI & LLM• Middle
Databases & Vector Storage• Middle
Microservices & API Architecture• Middle
Python• Middle
Industries
Artificial Intelligence• Middle
Technologies
Python• since 2022 • Middle
C#• since 2023 • Junior
Node JS• Middle
Rest API• since 2026
.NET• since 2023
Flask• since 2023
SQLAlchemy
FastAPI
Pydantic
Axios
Recommendations
- Build small to medium REST APIs that integrate LLM inference and file ingestion (use existing ai_service and FastAPI patterns).
- Develop database-backed features with SQLAlchemy and proper migrations and transaction handling (expand database.py and add Alembic-based migration history).
- Prototype AI-driven resume and text analysis features while hardening secrets management and adding error handling around external API calls.
- Improve operational maturity by adding logging, timeouts, retries with backoff, and basic observability (metrics/tracing) to services.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: