Hafijur Rhaman
- Citigroup Currency Exchange Dashboard: Led Python team designing RESTful APIs and data pipelines for global banking transaction system handling international currency transfers
- Customer Retention Modeling (Infosys): Engineered predictive churn model using XGBoost, SVC, Random Forest; feature engineering and model selection on multi-sector client data
- Financial Data Analysis Tool (Citigroup): Created Pandas/NumPy statistical analysis and reporting engine for financial dataset insights and visualization
- Sentiment Analysis NLP System: Built text classification pipeline using SVM, Random Forest, TF-IDF, NLTK to categorize opinions with high accuracy across product domains
Hafijur Rhaman
Hafijur brings 8+ years of progressive experience as a Python developer and ML specialist, evolving from backend engineering at Verizon and United Airlines to leading full-stack initiatives at enterprise firms like Cisco, Infosys, and Citigroup. His arc spans data engineering, NLP/computer vision, REST API design, and team leadership—combining production-grade Python with modern DevOps and cloud deployment.
Route to AI Engineer, Data Engineer, Data Science Engineer teams building high-throughput services in the candidate's core stack.
Where this candidate belongs
End-to-end Python ML pipelines (regression, classification, NLP, computer vision). RESTful API design and microservice architecture at scale. PostgreSQL/MySQL/NoSQL schema design and optimization under high throughput. Team leadership and mentorship in Agile environments.
Platform Engineer, Full-Stack Backend Engineer, Machine Learning Engineer, Data Engineer.
Remote-first. Hybrid considered for high-alignment local roles. Based in New York, US.
Targeting $80K+. Currently: Software Engineer at Pypes LLC.
Recruiter screen → hiring-manager review → technical conversation around Citigroup Currency Exchange Dashboard, Customer Retention Modeling (Infosys), Financial Data Analysis Tool (Citigroup).
Hafijur's work in Platform Engineer, Full-Stack Backend Engineer, Machine Learning Engineer translates directly into adjacent domains with similar architecture, scale, and reliability needs.
- Primary production depth is the candidate's core stack. Other tooling framed as ramp/adjacent unless proven.
- Leadership framing is technical leadership (mentorship, code review, architectural direction). Formal people-management should be validated per role.
- Additional interests are personal/research at present, not production claim.
Master Proof Matrix
This reusable proof index maps common senior/staff engineering requirements to candidate proof. Role-specific addenda extend — not replace — this base matrix.
Requirement: Deliver on citigroup currency exchange dashboard at production scale.
Proof: Led Python team designing RESTful APIs and data pipelines for global banking transaction system handling international currency transfers
Requirement: Deliver on customer retention modeling (infosys) at production scale.
Proof: Engineered predictive churn model using XGBoost, SVC, Random Forest; feature engineering and model selection on multi-sector client data
Requirement: Deliver on financial data analysis tool (citigroup) at production scale.
Proof: Created Pandas/NumPy statistical analysis and reporting engine for financial dataset insights and visualization
Requirement: Deliver on sentiment analysis nlp system at production scale.
Proof: Built text classification pipeline using SVM, Random Forest, TF-IDF, NLTK to categorize opinions with high accuracy across product domains
Requirement: Ship work that requires end-to-end python ml pipelines (regression, classification, nlp, computer vision).
Proof: Demonstrated at prior roles.
Requirement: Ship work that requires restful api design and microservice architecture at scale.
Proof: Demonstrated at prior roles.
Technical Proof Catalog
Led Python team designing RESTful APIs and data pipelines for global banking transaction system handling international currency transfers
Engineered predictive churn model using XGBoost, SVC, Random Forest; feature engineering and model selection on multi-sector client data
Created Pandas/NumPy statistical analysis and reporting engine for financial dataset insights and visualization
Built text classification pipeline using SVM, Random Forest, TF-IDF, NLTK to categorize opinions with high accuracy across product domains
Mid — role fit: adjacent.
Senior — role fit: primary.
Senior — role fit: primary.
Senior — role fit: secondary.
Visual Evidence Index
github.com/hafijur-python — public repositories, contribution activity, and open source project surfaces.
https://pypes.dev/hafijur-rhaman
Hosted candidate portfolio with positioning, proof points, and conversion kit surface for recruiter routing.
www.linkedin.com/in/hafijur-rhaman
Professional profile and recruiter connection path.
Contact Hafijur Rhaman
Route this candidate now.
This kit is designed to survive internal forwarding. The fastest next step is a recruiter screen, hiring-manager review, referral handoff, or direct call.
Primary fit: AI Engineer, Data Engineer, Data Science Engineer.
https://pypes.dev/hafijur-rhaman
Fastest way to reach Hafijur — grab a slot on their calendar.
Persistent candidate surface for routing, resume, and proof surfaces.
Professional profile and recruiter connection path.
Public engineering surface, repositories, and OSS project path.
Additional candidate-owned portfolio and technical writing surface.
Hafijur Rhaman is a New York, US-based AI Engineer. Hafijur brings 8+ years of progressive experience as a Python developer and ML specialist, evolving from backend engineering at Verizon and United Airlines to leading full-stack initiatives at enterprise firms like Cisco, Infosys, and Citigroup. His arc spans data engineering, NLP/computer vision, REST API design, and team leadership—combining production-grade Python with modern DevOps and cloud deployment. Best fit: AI Engineer, Data Engineer, Data Science Engineer.