Adnan Abdullah — CTO

Agentic AI Engineer and AI Team Lead with 2 years of production experience building multi-agent systems, voice AI pipelines, and Business automation at Oladoc — Pakistan's largest digital health platform. Joined as the sole AI engineer, shipped 7 production systems entirely alone, and was promoted to AI Team Lead within 18 months based on delivery and business impact. I'm Adnan — an Agentic AI Engineer building multi-agent systems, voice bots, and LLM pipelines for healthcare and customer support. Not prototypes. Not demos. Production systems, every day. Flagship — Zoya, AI Health Coach A personalized AI health coach for Pakistan — a market global AI overlooks. Speaks fluent Urdu (voice in, out), more friend than chatbot. → Personalized meal plans around Pakistani kitchens, culture, and goals → Tracks activity, nutrition, sleep, and stress — turning data into smart recommendations for a healthier life → Voice-first in Urdu and English — speak naturally, get voice replies → Long-term memory of preferences, goals, cycles, and progress → Daily check-ins, food logging, fitness, and habits — 24/7 on WhatsApp Zoya proves AI can deliver culturally native coaching at a fraction of human-coach cost — fit for any market where personalization and language are the bottleneck. At Oladoc — Pakistan's #1 digital health platform — sole AI engineer across the full lifecycle: → Multi-agent WhatsApp chatbot handling 85% of all patient support autonomously — books appointments, schedules lab tests, reschedules and cancels, answers FAQs, and stays strictly in scope. 2,000+ daily bookings. → Call analyzer processing 8,000+ recordings daily — scoring agents, catching wrongful bookings. → Prescription digitizer — handwriting to structured records + Urdu voice summary. → Lab report analyzer — complex results into plain language + Urdu voice summary. Stack: LangGraph · LangChain · LangSmith · Multi agent system · FastAPI · RAG · Voice AI · Docker · AWS · n8n What I've built — and can build again: → Workflow automation eliminating repetitive tasks at scale. → 24/7 appointment booking and customer support agents. → Voice bots in English, Urdu, and regional languages. → Healthcare AI — prescriptions, lab analysis, clinical workflows. → Multilingual agentic systems production-grade. Everything I ship works in production — not a demo that breaks on the second prompt. Currently open to full-time remote Agentic AI Engineer or AI Tech Lead roles with international teams building real production systems — where ownership, scale, and impact actually matter.

Stackforce AI infers this person is a Healthcare AI Engineer with expertise in multi-agent systems and voice automation.

Location: Lahore, Punjab, Pakistan

Experience: 11 mos

Skills

Ai Team Leadership
Multi-agent Systems
Voice Ai
Ai Automation
Healthcare Ai
Ai Compliance
Quality Assurance
Workflow Automation
Data Processing
Machine Learning
Data Analysis

Career Highlights

Promoted to AI Team Lead within 18 months.
Delivered 7 production AI systems independently.
Achieved 90% reduction in customer support workload.

Work Experience

oladoc

AI Team Lead (2 mos)

Agentic AI Engineer (9 mos)

Agentic AI Engineer (8 mos)

CodeAlpha

Machine Learning Intern (3 mos)

Prodigy InfoTech

Machine Learning Intern (1 mo)

Education

Bachelor of Engineering - BE at National University of Sciences and Technology (NUST)

Adnan Abdullah

CTO

Lahore, Punjab, Pakistan11 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Promoted to AI Team Lead within 18 months.
Delivered 7 production AI systems independently.
Achieved 90% reduction in customer support workload.

Stackforce AI infers this person is a Healthcare AI Engineer with expertise in multi-agent systems and voice automation.

Contact

Skills

Core Skills

Ai Team LeadershipMulti-agent SystemsVoice AiAi AutomationHealthcare AiAi ComplianceQuality AssuranceWorkflow AutomationData ProcessingMachine LearningData Analysis

Other Skills

LangGraphLangChainAI agent orchestrationAWSDockerSTT/TTSAI Conversation Compliance AnalyzerAI Conversation ComplianceOCR NER modelAutomationTensorFlowCNNAnthropic ClaudeChatGPTLarge Language Model Operations (LLMOps)

About

Experience

11 mos

Total Experience

11 mos

Average Tenure

11 mos

Current Experience

Oladoc

3 roles

AI Team Lead

Promoted

Apr 2026 – Present · 2 mos

Promoted to AI Team Lead, leading a team of 3 engineers. Work directly with CEO on AI product strategy and with CTO on system architecture and deployment. Own end-to-end delivery of all production AI systems while scaling existing infrastructure and shipping new agentic AI features across the Oladoc platform.
Key responsibilities:
Leading team of 3 engineers on scalability, system improvements, and new feature development.
Building outbound AI voice call agent for automated Urdu sales calls replacing 80+ human agents
Integrating real-time smartwatch biometric data into AI health coaching systems
Coordinating with CTO on infrastructure decisions, deployment pipelines, and system architecture
Translating CEO business requirements directly into technical architecture and delivery plans
Scaling multi-agent WhatsApp systems handling 2,000+ daily bookings and 8,000+ call audits/day
Key achievements:
Promoted within 18 months based on sole ownership of 7 production AI systems
85% reduction in wrong booking complaints
Sales operations team reduced from 10 to 3 through AI automation
90% reduction in customer support workload through agentic automation

LangGraphLangChainAI agent orchestrationAWSDockerAI Team Leadership+1

Agentic AI Engineer

Promoted

Jun 2025 – Mar 2026 · 9 mos

Key Responsibilities & Impact
Designed and deployed multi-agent LLM systems using LangGraph & LangChain for doctor appointment booking, lab scheduling, and patient support.
Built an LLM-powered doctor description generator to create structured, readable summaries for patient prescriptions.
Developed an AI-generated voice prescription summary in Urdu, allowing patients to hear dosage instructions and test recommendations (reduced support queries by 35%).
Integrated voice pipelines (STT/TTS) to enable voice-driven patient interactions and automated call reminders.
Built an AI Conversation Compliance Analyzer that evaluates call recordings for:
user consent issues
agent performance
policy clarity
pricing explanation quality
(Result: Reduced false subscriptions by 70% and improved QA auditing speed by 60%.)
Automated video-consultation reminders using Infobip APIs, sending voice messages when patients miss their scheduled time (reduced no-shows by 25%).
Improved internal workflows through LLM integrations, API automation, vector databases, and custom orchestrations.

LangGraphLangChainSTT/TTSAI Conversation Compliance AnalyzerMulti-agent SystemsVoice AI

Agentic AI Engineer

Sep 2024 – May 2025 · 8 mos

Developed an Attendance Automation System using fingerprint logs that auto-calculates punctuality and emails employees their daily attendance
(Result: Reduced manual HR effort by 90%).
Built a Keyword SEO Rank Tracker that monitors daily search ranking positions and sends automated reports to the SEO team
(Result: Saved 1–2 hours/day of manual work).
Created an OCR-based handwritten prescription digitizer to convert messy doctor handwriting into clean, standardized digital PDFs stored in the internal database.
Developed an AI-powered YouTube description generator that extracts audio, performs speech-to-text conversion, and automatically produces SEO-optimized titles and descriptions.
Conducted internal workshops to train teams on leveraging AI tools for workflow automation, productivity enhancement, and repetitive task reduction.

OCR NER modelAutomationData ProcessingWorkflow Automation

Codealpha

Machine Learning Intern

May 2024 – Aug 2024 · 3 mos · Lucknow, Uttar Pradesh, India · Remote

Worked on Ensemble Learning techniques including Random Forest, Bagging, AdaBoost, and Gradient Boosting, applying them to real-world datasets to improve predictive performance.
Completed multiple machine learning projects across classification, clustering, and regression using Python, Scikit-learn, NumPy, and Pandas.
Developed a Bagging-based model to improve model stability on noisy datasets
(Result: Accuracy improved by 12–18% over baseline).
Built Boosting pipelines (AdaBoost/XGBoost-style) to strengthen weak learners and enhance predictive power
(Result: Significant gains in precision and recall).
Implemented a Random Forest classifier that reduced overfitting and improved decision boundaries
(Result: ROC-AUC improved by 15%).
Performed feature engineering, data preprocessing, and model evaluation using industry-standard ML workflows.
Strengthened understanding of supervised learning, ensemble strategies, and end-to-end experimentation through hands-on model development.

TensorFlowCNNMachine LearningData Analysis

Prodigy infotech

Machine Learning Intern

Apr 2024 – May 2024 · 1 mo · Mumbai, Maharashtra, India · Remote

Completed multiple end-to-end ML projects, including an Image Recommendation System, House Price Prediction Model, and CNN-based Flower Classifier.
Built an Image Recommendation System using feature extraction and similarity metrics
(Result: Delivered high-relevance image matches with strong cosine-similarity alignment).
Developed a House Price Prediction model using regression techniques, feature engineering, and model tuning
(Result: Achieved high prediction stability with improved RMSE over baseline models).
Created a deep-learning Flower Classification model using CNNs to accurately categorize image datasets
(Result: Model accuracy exceeded expected benchmarks for the task).
Applied Python, Scikit-learn, TensorFlow, and NumPy for data preprocessing, model development, evaluation, and deployment-ready code.
Demonstrated strong problem-solving and coding skills in a remote environment, consistently delivering high-quality, well-structured solutions.
Recognized for exceeding expectations and completing all assigned ML tasks ahead of deadlines.