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Enterprise AI/ML Engineer

Enterprise AI systems, engineered for production.

I design and ship RAG platforms, AI agents and LLM integrations for legal-tech, public-sector, healthcare and fintech teams, from architecture to production, including a 50,000-document legal intelligence platform.

Book a free discovery callView case studies
Top Rated · 100% Job Success
Years shipping production AI
5+
Job Success on Upwork
100%
Average client rating
5.0★
Documents in production RAG
50K+
  • Legal-tech
  • Government & Public Sector
  • Healthcare & Digital Health
  • Fintech & Financial Services
  • SaaS & Startups
  • Customer Support & E-commerce
  • Cloud & DevOps
Services

End-to-end AI engineering, from first prototype to production.

Seven ways I help teams turn AI from a slide into a system that their customers and staff rely on every day.

Selected case studies

Production systems, not prototypes.

Each one shipped to real users, under real constraints: sensitive data, regulated domains and traffic that doesn't wait.

All 12 case studies
legal-search · data-greffe-justice
Hybrid · RBAC
résiliation abusive d’un bail commercial, OHADA

50K+ décisions· < 2 s· BM25 + vecteurs· réponses citées

  • Cour d’appel · Commercial0.94

    …la juridiction relève que [PARTIE A] a procédé à la résiliation du bailsans préavis, en méconnaissance de l’Acte uniforme OHADArelatif au droit commercial général…

    [1] Arrêt n° ••• · chaîne d’appel ↗

  • Tribunal de commerce · Bail0.86

    …[PARTIE B], preneur, sollicite des dommages-intérêts pour rupture abusive…

rôle : juristePII masquées3 index
Illustrative interface · client data never shown
01Data Greffe JusticeLive in production

Enterprise Legal Intelligence Platform

A production RAG platform that turns 50,000+ French-language court decisions into a searchable, privacy-safe knowledge base, with triple-search retrieval, role-based access, compliance-grade PII anonymization and AI-assisted drafting.

Legal documents indexed
50K+
PII detection accuracy
98%
Query response time
<2s
Read case study
anonymizer · triple-search
98% detection

Original

Le mineur Koffi A., domicilié à Cocody, rue des Jardins, représenté par sa mère Awa D.… K. Adjouaconfirme…

Anonymized

Le mineur [MINEUR 1], domicilié à [ADRESSE], représenté par sa mère [PARTIE B]… [MINEUR 1]confirme…

  • Pass 1Contextual entities (Claude)
  • Pass 2Patterns & identifiers
  • Pass 3Variants & later mentions

4 entities · 1 variant spelling caughtminor-protection rules applied

Illustrative interface · client data never shown
02Legal Anonymization Engine

Multi-Jurisdiction PII Anonymization Engine for Court Decisions

A compliance-grade anonymization engine for legal case decisions. A triple-search architecture finds personal data that single-pass detection misses, reaching 98% detection accuracy with dedicated safeguards for minors and vulnerable parties.

PII detection accuracy
98%
Independent search passes
3
Decisions anonymized before release
100%
Read case study
civic-assistant · dual endpoint
10K+/day
“When does the youth scholarship program expand?”temporal router → planned

Publicns: public

The expansion is part of the next program cycle. Eligibility criteria and official announcements are listed on the program page.

2 public sources

Field teamns: field

Rollout guidance for coordinators: priority regions, required documents and the household briefing checklist.

3 field sources · restricted

one knowledge base · two namespacesno cross-audience leakage

Illustrative interface · client data never shown
03Civic Intelligence Assistant

High-Volume Public-Sector RAG Assistant

A dual-endpoint RAG assistant that serves the public and the field teams of a national political organization from one governed knowledge base. It is sized for 10,000+ queries a day, with temporal query routing and namespace-isolated retrieval.

Queries per day capacity
10K+
Audience-specific endpoints
2
Governed knowledge base
1
Read case study
whatsapp · ai-sponsor
24/7
0:14

whisper → “Tonight is hard. Can we talk for a minute?”

0:19

I’m here. Let’s take it one step at a time. What’s on your mind?

prompt cache · input tokens≈90% saved

Illustrative interface · client data never shown
04AI Sponsor

Multi-Channel AI Support SaaS Platform

The backend for a 24/7 AI sponsor that supports people in 12-step recovery over chat and WhatsApp voice. It combines Claude-powered conversations, Stripe subscription billing and a prompt-caching architecture that cut LLM input costs by about 90%.

LLM input-cost reduction
~90%
Chat and voice availability
24/7
Automated test categories
12
Read case study
agent-run · langgraph + mcp
HITL

  1. Assess Q3 exposure for portfolio Alanggraph · state

  2. web_search("Q3 earnings guidance")mcp · ok

  3. read_docs("risk-policy.pdf")mcp · ok

  4. get_financial_data(account="A-17")awaiting approval

    Sensitive tool. Execution paused by a graph interrupt.

    ApproveReject

state checkpointed · resumes exactly here after a decision

Illustrative interface · client data never shown
05MCP Agent Platform

Agentic Orchestration Platform with LangGraph and MCP

A stateful agent platform on LangGraph that reaches live tools through the Model Context Protocol, remembers context across sessions and pauses for human approval before any sensitive action runs.

Approval gates on sensitive actions
HITL
Checkpointed multi-session memory
Durable
Plug-in tool federation
MCP
Read case study
cloudops-agent · amazon bedrock
Confirm-first
Create a private S3 bucket for invoices in Ireland, with versioning.

I’ll create this resource. Please confirm:

Bucket
invoices-archive-eu
Region
eu-west-1
Access
Private, public access blocked
Versioning
Enabled
Confirm this change? (yes/no)Yes, createNo

boto3 call runs only after an explicit yes · claude 3.5 sonnet on bedrock

Illustrative interface · client data never shown
06CloudOps Agent

Natural-Language Cloud Operations Agent on Amazon Bedrock

A chat-driven AWS operations agent on Amazon Bedrock that lets non-engineers create, inspect, update and delete cloud resources in plain language, with validation and confirm-before-execute guardrails on every change.

Changes require explicit approval
100%
Full resource lifecycle in plain language
CRUD
Claude 3.5 Sonnet on AWS
Bedrock
Read case study

AI project estimator

Get a tailored project plan in 60 seconds.

Five questions about what you’re building. Instantly see the recommended architecture, delivery phases and a realistic timeline, then email it to yourself.

  • Architecture
  • Phased plan
  • Timeline

Step 1 of 5 · What are you building?

Start the estimator
Capabilities

The full AI stack, not a slice of one.

104+ tools and techniques across models, agents, retrieval, data, cloud and automation, so one engineer owns the whole pipeline and nothing falls between the gaps.

Generative AI & LLMs

16 tools & techniques

Frontier and open-source models, integrated for quality, safety and cost.

  • Claude API
  • Claude Code
  • Claude Agent SDK
  • OpenAI GPT-4o
  • Amazon Bedrock
  • Google Gemini
  • Hugging Face Transformers
  • Ollama
  • vLLM
  • LoRA / QLoRA fine-tuning
  • Prompt engineering
  • Prompt caching
  • Structured outputs
  • Tool / function calling
  • Embeddings
  • Guardrails
How we work

A delivery process built for zero surprises.

Clear milestones, working demos every week and measurable quality gates, so you always know what's done, what's next and what it costs.

  1. 01

    Discover

    A free 30-minute call to understand the problem, the data and what success looks like.

    You getClear problem statement and success metrics

  2. 02

    Architect

    A written technical plan: architecture, model and vector-store choices, risks, milestones and a fixed quote.

    You getArchitecture document and milestone plan

  3. 03

    Build

    Iterative delivery in short milestones with working demos, so you see progress every week, not at the end.

    You getWorking increments you can test

  4. 04

    Evaluate & harden

    Measured against an evaluation set, then hardened with guardrails, access control, error handling and load testing.

    You getEvaluation report and test suite

  5. 05

    Deploy & support

    Dockerized deployment to your cloud, documentation and handover, with optional ongoing support and improvement.

    You getProduction system plus documentation

Ways to work together

Start small, ship fast, scale with confidence.

Most clients begin with a Discovery Sprint. You get a written architecture and a fixed quote before committing to the build.

Discovery Sprint

1–2 weeks

For teams validating an idea or de-risking a build.

  • Architecture and feasibility review
  • Proof of value on your real data
  • Risks, costs and model choices made explicit
  • Written plan with milestones and a fixed quote

A clear go/no-go decision and a build plan you own.

Build

Milestone-based

For teams ready to ship a production system.

  • Weekly demos on working software
  • Evaluation suite, guardrails and monitoring
  • Deployment to your cloud
  • Documentation and handover to your team

A production system, fixed-price milestones or hourly.

Most common

Partner

Monthly

For teams improving and scaling after launch.

  • New features and quality improvements
  • Evaluation and cost reviews
  • Priority support
  • Architecture advice for your engineers

A senior AI engineer on call, without a full-time hire.

Book a free discovery call Estimate your project first
Client feedback

Trusted on time-sensitive, highly technical work.

Top Rated on Upwork with 100% Job Success and a 5.0★ average across 23 contracts.

“It has been a real pleasure working with Mubashir on this highly technical and time-sensitive legal-tech project. From the outset, he demonstrated strong professionalism and deep technical knowledge.”
Upwork clientLegal-tech backend enhancement
“Collaborating with Mubashir, an exceptional AI engineer, has been truly inspiring. He consistently delivers top-notch results without compromising on deadlines or quality. His exceptional communication skills make complex AI concepts easy to understand.”
CEO, Giant Tech SolutionsFormer employer
“Muhammad was great, he went above and beyond the scope of work and truly cares about giving his clients the best experience - he is also an AI expert.”
Upwork clientAI specialist / custom GPT build
“Muhammad consistently demonstrated exceptional availability and support throughout the design and study phases of my project, which involved creating a chatbot. His expertise in this area is evident and invaluable.”
Upwork clientDeep learning lab · chatbot
“He is extremely knowledgeable and professional. I recommend him 100%.”
Upwork clientAI consultation

More reviews on my Upwork profile.

Muhammad Mubashir, Enterprise AI/ML Engineer

Muhammad Mubashir

Enterprise AI/ML Engineer

Peshawar, PK
Remote · worldwide

About

One engineer, accountable for the whole system.

I’m Muhammad Mubashir, an AI/ML engineer with 5+ years of building systems that have to work outside the demo. That has meant a 50,000-document legal platform, a public-sector assistant sized for 10,000+ queries a day, and AI products with real billing and real users.

I own the full stack: retrieval architecture, prompts and evaluation, the API, the deployment. No handoffs, and no gap between “it works in the demo” and “it works in production.”

  • Production over prototypes

    A demo that works on ten documents proves nothing. I design for real data volumes, real users and real failure modes from day one.

  • Evaluate before you optimize

    Every system gets an evaluation set built with your domain experts, so each change is measured, not guessed.

  • Humans stay in control

    Agents propose, people approve. Sensitive actions sit behind explicit approval gates, and every step is traceable.

  • Security and compliance first

    Least-privilege access, PII anonymization and role-based retrieval are part of the architecture, not a patch at the end.

More about me
  • Claude
  • OpenAI
  • LangChain
  • LangGraph
  • CrewAI
  • MCP
  • Pinecone
  • Elasticsearch
  • Qdrant
  • PyTorch
  • TensorFlow
  • Hugging Face
  • FastAPI
  • Node.js
  • Next.js
  • Docker
  • Amazon Bedrock
  • PostgreSQL
  • n8n
  • Stripe
  • Twilio
Insights

Notes from building AI in production.

Architecture decisions, trade-offs and lessons learned: RAG, agents, cost control and evaluation.

All articles
FAQ

Questions clients usually ask first.

What kind of AI projects do you take on?

Production AI systems: RAG platforms over large document sets, AI agents and multi-agent workflows, custom chatbots and LLM features, document processing and PII anonymization, voice agents, AI automation and custom machine learning. If it needs to work reliably for real users, it's a fit.

Do you work with startups or enterprises?

Both. I've built for early-stage SaaS founders and for organizations with strict privacy, access-control and compliance needs. The engineering standard is the same; the scope and governance scale with you.

How does an engagement start?

With a free 30-minute discovery call. You then get a written scope with architecture, milestones and a fixed quote or hourly estimate, whichever suits the project, before any commitment.

Which time zones do you work with?

I'm based in Peshawar, Pakistan (PKT (UTC+5)) and work remotely with teams worldwide. I keep regular overlap hours for calls and share async progress updates, and I reply within 24 hours.

Will you sign an NDA? Who owns the code?

Yes, I'm happy to sign an NDA before we discuss details. You own the code, models and documentation delivered for your project.

Can I hire you through Upwork?

Yes. If you prefer Upwork's payment protection, you can hire me there (Top Rated, 100% Job Success), or we can contract directly.

Available for new engagements

Have an AI initiative in mind? Let’s scope it in 30 minutes.

Free discovery call. You'll leave with a clear view of the architecture, risks and next steps, whether or not we work together. I reply within 24 hours.

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