You will get Design, Build & Deployment of Agentic AI + Automation for Your Workflow
Rising Talent
Rising Talent
Project details
I build automation and agentic-AI systems that ship fast, work reliably, and are measurable from day one. You get a clean, goal-driven scope (set around your KPI), a secure Python backend (FastAPI/Django), and agents that plan + execute tasks with proper guardrails, logging, and API access. Each project is packaged as a fixed-price tier with clear deliverables, timelines, and revisions so you always know what’s included and when it lands.
To keep momentum, I request essential inputs at purchase (goal/KPI, data access, deploy preference). If required items aren’t submitted in time, Upwork can auto-cancel, protecting both schedule and budget. Once delivered, you have 14 days to review or request changes; otherwise escrow releases automatically, clear, low-friction approvals.
What sets this apart: production-ready delivery (docs, env templates, API/Swagger, Docker), safety-minded design (least-privilege access, input hardening), and optional add-ons (extra integrations, dashboards, rush). You leave with a maintainable system, not a demo, deployed to AWS or your server and ready to scale.
To keep momentum, I request essential inputs at purchase (goal/KPI, data access, deploy preference). If required items aren’t submitted in time, Upwork can auto-cancel, protecting both schedule and budget. Once delivered, you have 14 days to review or request changes; otherwise escrow releases automatically, clear, low-friction approvals.
What sets this apart: production-ready delivery (docs, env templates, API/Swagger, Docker), safety-minded design (least-privilege access, input hardening), and optional add-ons (extra integrations, dashboards, rush). You leave with a maintainable system, not a demo, deployed to AWS or your server and ready to scale.
Machine Learning Tools
Apache Mahout, Apache Spark, Apache Spark MLlib, Azure Machine Learning, BERT, ChatGPT, Databricks Platform, GitHub Copilot, Google Sheets, H2O, Keras, Microsoft Excel, MLflow, NLTK, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Scrapy, TensorFlow, Tesseract OCRWhat's included
| Service Tiers |
Starter
$750
|
Standard
$2,000
|
Advanced
$4,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 25 days |
Number of Revisions | 2 | 3 | 4 |
Number of Model Variations | 1 | 3 | 5 |
Number of Scenarios | 2 | 5 | 8 |
Number of Graphs/Charts | 3 | 6 | 10 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$50
Additional Model Variation
(+ 2 Days)
+$200
Additional Scenario
(+ 1 Day)
+$120
Additional Graph/Chart
+$60
Data Source Connectivity
(+ 2 Days)
+$250About Muhammad
Quantitative Analyst | Python Backtesting, Factor Research & Financial
Westborough, United States - 3:16 pm local time
My foundation spans computer science and quantitative finance, and my focus is singular: building robust, bias-aware, data-driven systems that translate financial theory into executable research and models.
Every project I work on, whether it is constructing point-in-time financial datasets, designing quantitative signals, or validating strategies through rigorous backtesting, is approached with discipline and intent. I believe meaningful results come not from over-optimization, but from careful modeling, statistical validation, and respect for market structure.
Currently pursuing my MS in Quantitative Finance at Northeastern University (Boston), I bridge theory and execution by combining the statistical rigor of a quantitative researcher with the engineering mindset of a software developer. My work emphasizes correctness, transparency, and repeatability over curve-fitting or surface-level performance.
My primary areas of focus include:
Algorithmic Trading & Quantitative Research
-Designing and evaluating trading signals using time-series analysis, stochastic modeling, and structured backtesting workflows.
Factor Models & Risk Analytics
-Empirical asset pricing, portfolio optimization, systematic risk factors, and performance measurement grounded in statistical finance.
Financial Data Engineering
-Building scalable, point-in-time datasets, handling survivorship and look-ahead bias, and developing clean pipelines for quantitative research.
To me, finance is applied mathematics under uncertainty, and technology is the tool that makes disciplined experimentation possible. Every dataset, model, and result is a step toward understanding how markets behave, not in hindsight, but in real conditions.
If you value precision, statistical integrity, and research-driven execution, we are already aligned.
Core Expertise
Quantitative Research & Analysis:
-Time-series modeling, factor research, signal construction, backtesting frameworks, performance and risk metrics
Programming & Data Science:
-Python (NumPy, pandas, scikit-learn, QuantLib), SQL, Jupyter, statistical computing
Mathematical & Statistical Methods:
-Probability, regression, optimization (LP/QP), Monte Carlo simulation, stochastic processes, Kalman filtering
Financial Engineering:
-Portfolio optimization, derivatives concepts, financial econometrics, empirical asset pricing
Engineering & Tooling:
-Git, HDF5, Bloomberg Terminal, AWS-based data workflows, reproducible research environments
Let’s discuss your problem, define the right analytical approach, and build something.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements intake
Collect goal/KPI, users, data access, deploy choice.
Kickoff & scope lock
Align on tier, deliverables, timelines, comms inside the Upwork workroom.