You will get MLOps & LLMOps Setup | Evals, Tracing, CI/CD & Cost Monitoring
Project details
Models do not fail in the demo. They fail in production, quietly, while the bill grows. I will build the MLOps and LLMOps foundation that keeps your AI measurable, observable, and affordable.
What you get:
• Evaluation pipelines: automated eval suites for your LLM features and models, wired into CI/CD
• Observability: LangSmith tracing, structured logging, latency and error dashboards
• Model and cost monitoring: token spend, drift, and quality regressions caught early
• Deployment: containerized model and LLM services with Docker, Kubernetes, and CI/CD on AWS or GCP
• Registry and versioning for models, prompts, and datasets
• Runbooks and docs so your team operates it without me
Also available as an audit: I review your current AI stack and deliver a prioritized hardening plan.
8+ years of enterprise SaaS engineering across MLOps, DevOps, and SecOps. If your AI is in production and you cannot measure it, that is the risk. Message me with your stack and I will tell you exactly what is missing.
What you get:
• Evaluation pipelines: automated eval suites for your LLM features and models, wired into CI/CD
• Observability: LangSmith tracing, structured logging, latency and error dashboards
• Model and cost monitoring: token spend, drift, and quality regressions caught early
• Deployment: containerized model and LLM services with Docker, Kubernetes, and CI/CD on AWS or GCP
• Registry and versioning for models, prompts, and datasets
• Runbooks and docs so your team operates it without me
Also available as an audit: I review your current AI stack and deliver a prioritized hardening plan.
8+ years of enterprise SaaS engineering across MLOps, DevOps, and SecOps. If your AI is in production and you cannot measure it, that is the risk. Message me with your stack and I will tell you exactly what is missing.
Machine Learning Tools
Amazon SageMaker, Apache Mahout, Apache Spark, Apache Spark MLlib, Azure Machine Learning, GitHub Copilot, Kubeflow, MLflow, NumPy, NVIDIA AI Platform, OpenCV, PyMC, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, SQL, TensorFlowWhat's included
| Service Tiers |
Starter
$700
|
Standard
$1,800
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 3 | 5 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
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DS
David S.
Jul 13, 2026
Senior AI Full Stack Engineer for AI/LLM Integration into Enterprise SaaS Platform
Honestly one of the smoothest experiences I have had on Upwork. Muneeb did an excellent job integrating AI/LLM features into our existing platform. He did exactly what we agreed, finished within the estimated hours, and communicated well. Rare to find someone this straightforward. Will work with him again soon.
SI
Shoaib I.
Aug 21, 2023
Test task for a full time job
Muneeb is a responsible developer, good at communication and always ready to make updates. I recommend him for MERN jobs.
DH
Douglas H.
Apr 13, 2021
Senior Full stack Developer for Web App
I had great experience with this freelancer. He went beyond my requirements, super fast, skilled and highly recommended for other clients.
RU
Rana U.
Sep 15, 2020
Vue.js and Laravel developer is needed
He is professional, attentive and on more than one occasion, has gone the extra mile to ensure
the work is done right. We have been working on this project for a few months and he has
always been fantastic to work with. We will continue to come back for future projects.
the work is done right. We have been working on this project for a few months and he has
always been fantastic to work with. We will continue to come back for future projects.
SH
Samir H.
Jun 28, 2020
Price comparison, promo codes, deals, product search app and website architecture plan and costs
About Muneeb Ahmad
Full-Stack AI Engineer | Document Intelligence, OCR, LLM, RAG, Python
Islamabad, Pakistan - 3:23 pm local time
I'm a Full-Stack AI Engineer (MS in Artificial Intelligence, 10+ years building production software) specializing in Document Intelligence and Intelligent Document Processing (IDP), data extraction pipelines (OCR & LLM), RAG document Q&A with source citations, and complete document-processing SaaS platforms.
𝐈𝐍𝐃𝐔𝐒𝐓𝐑𝐈𝐄𝐒 𝐈 𝐒𝐄𝐑𝐕𝐄
● Legal: contract analysis and clause extraction, legal RAG with citations, case-document processing, Clio/Filevine integrations
● Healthcare: HIPAA-aware medical-record pipelines, PHI redaction before any AI call, claims document automation
● Banking & Finance: bank-statement extraction with running-balance reconciliation, loan/KYC document processing, financial statement data
● Accounting & AP: invoice and receipt extraction into QuickBooks/Xero, accounts-payable automation
● Insurance, Real Estate & Logistics: claims packets, lease abstraction, shipping and customs documents
𝐖𝐇𝐀𝐓 𝐈 𝐁𝐔𝐈𝐋𝐃
● Extraction pipelines for PDFs, scans, and images: ingest → classify → extract (Azure Document Intelligence, AWS Textract, GPT-4o/Claude vision) → validate → deliver into your API, sheets, ERP, or CRM
● RAG systems: question-answering over your document base where every answer cites its source page (pgvector, Pinecone, hybrid search)
● Document SaaS platforms end-to-end: multi-tenant architecture, auth/RBAC, Stripe billing, admin dashboards, human-review queues, plus the AI layer
𝐖𝐇𝐘 𝐌𝐘 𝐏𝐈𝐏𝐄𝐋𝐈𝐍𝐄𝐒 𝐀𝐑𝐄 𝐃𝐈𝐅𝐅𝐄𝐑𝐄𝐍𝐓 (this is where most document AI fails)
● Benchmark-first: we hand-label a golden dataset on day one, so every accuracy claim is measured, not promised
● Confidence-scored: every extracted field carries a confidence score; low-confidence items route to human review instead of silently exporting wrong data
● Audited: full audit logs and source citations on every AI answer
● Honest by design: pipelines engineered to flag "not found" instead of inventing data, so wrong answers never reach your records silently
● Cost-engineered: classification routes each document to the cheapest capable method. I quote cost per document, not just accuracy
𝐘𝐎𝐔𝐑 𝐃𝐀𝐓𝐀 𝐒𝐓𝐀𝐘𝐒 𝐘𝐎𝐔𝐑𝐒
I develop on public, synthetic, or redacted samples; production runs in YOUR cloud (AWS/Azure/VPC) with your API keys and your storage. Your data never trains models. NDA-ready, encryption at rest and in transit, access logging, written deletion policy.
𝐓𝐇𝐄 𝐅𝐔𝐋𝐋-𝐒𝐓𝐀𝐂𝐊 𝟖𝟎/𝟐𝟎 𝐀𝐃𝐕𝐀𝐍𝐓𝐀𝐆𝐄
The AI is ~20% of a document product; the other 80% is architecture, APIs, billing, dashboards, and deployment. 10+ years of full-stack engineering (Python/FastAPI, Next.js/React, Node.js, PostgreSQL, AWS/Docker/Kubernetes) means I ship the whole product, not a demo.
𝐑𝐄𝐂𝐄𝐍𝐓 𝐖𝐎𝐑𝐊
✔ Paxton: Built an AI legal assistant for lawyers covering legal research, document drafting, file analysis, and workflow acceleration through document-aware, natural-language AI
✔ Provider Note: Built an AI clinical documentation and medical coding platform for health systems: real-time transcription (Whisper/Deepgram), LLM note structuring, RAG pipelines, HIPAA-compliant data handling
✔ FlowMind: Built an AI document evaluation, scoring, and intelligent routing system (GPT-4 + n8n + FastAPI) that eliminated manual document workflows
✔ Built a production AI pipeline processing thousands of items daily, with an automated quality gate that cut manual review to near zero
𝐒𝐓𝐀𝐑𝐓 𝐒𝐌𝐀𝐋𝐋, 𝐒𝐄𝐄 𝐏𝐑𝐎𝐎𝐅 𝐅𝐀𝐒𝐓
Send me one document type and what you need out of it. Within 24 hours you'll have honest feasibility notes and the accuracy target I'd commit to. In 1–2 weeks, a fixed-price paid pilot gives you measured results, not promises. Your documents can start processing themselves this month. Click Message and let's scope your pilot.
Keywords: Document Intelligence, Intelligent Document Processing, IDP, Data Extraction, OCR, PDF Data Extraction, Document Parsing, Document Automation, Invoice Processing, Bank Statement Extraction, Contract Analysis, Legal AI, Medical Records AI, Claims Processing, RAG, Retrieval Augmented Generation, LLM Integration, OpenAI, GPT-4, Claude, LangChain, LangGraph, Azure Document Intelligence, AWS Textract, Document Q&A, Chat with PDF, Semantic Search, Vector Database, pgvector, Pinecone, Python, FastAPI, Next.js, React, PostgreSQL, AI SaaS, Multi-tenant SaaS, Stripe, HIPAA, n8n, Make, Zapier, AI Automation, MLOps, AI Agent Development
Steps for completing your project
After purchasing the project, send requirements so Muneeb Ahmad can start the project.
Delivery time starts when Muneeb Ahmad receives requirements from you.
Muneeb Ahmad works on your project following the steps below.
Revisions may occur after the delivery date.
Audit the current stack and define the metrics that matter.
Implement evals, tracing, and cost monitoring wired into CI/CD.