A.Q Builds Applied AI and ML Systems

Director of Engineering at Digital Miles. 10+ years of experience building software and applied AI/ML, forecasting, and decision systems.

Portrait of A.Q.
Recognition

Signals that the work shipped and mattered

A clean snapshot of outcomes across applied AI, forecasting, analytics, and decision systems.

AI

Production-grade ML systems

End-to-end delivery: data, modeling, evaluation, deployment, and monitoring.

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TS

Forecasting and seasonality

Time-series pipelines using rolling windows, hierarchies, and campaign effects.

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UX

Decision support, not demos

Insights that reduce manual reporting and help leadership act faster.

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ENG

10+ years engineering depth

Systems thinking across backend, product, reliability, and ML operations.

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Projects

Selected applied AI and ML work

Company-backed projects focused on forecasting, behavioral analytics, decision support, and computer vision.

Digital Miles
Digital MilesApollo AI for Telenor
Telecom sales intelligenceTime-series forecastingPredictive analytics

Sales AI Agent

Led design and engineering of an AI-powered sales intelligence platform built on time-based sales data. The system analyzes historical performance, rolling trends, and campaign cycles to forecast outcomes across the sales hierarchy.

ApolloForecasting + scoring
Daily/weekly/monthlyCampaign + seasonal effectsRSO → ASM → ZM
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Key capabilities

  • Time-based performance tracking (daily, weekly, monthly)
  • Seasonal and campaign-driven trend analysis
  • Hierarchical forecasting (RSO, ASM, Zonal Manager)
  • Next-month and near-term sales predictions

Models and techniques

  • Time-series forecasting models
  • Regression with temporal features
  • Seasonal trend analysis
  • Rolling-window performance scoring

Impact

  • Enabled proactive, data-driven leadership decisions
  • Improved visibility into seasonal dips and peaks
  • Reduced dependency on manual reporting
Digital MilesMyHisaab
FintechBehavioral analyticsConversational AI

A WhatsApp finance assistant built on behavior

Led engineering for an AI-powered personal finance assistant. Uses time-based financial data to understand spending behavior and predict upcoming expenses.

MyHisaabTrends + predictions
Time-based trackingRecurring patternsForecasting
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Key capabilities

  • Time-based expense tracking and categorization
  • Weekly and monthly spending trend analysis
  • Recurring and seasonal expense detection
  • Predictive insights into upcoming behavior

Models and techniques

  • Time-series analysis of transaction data
  • Pattern detection over rolling periods
  • NLP-based expense classification
  • Behavioral trend modeling

Impact

  • Improved long-term visibility into financial habits
  • Enabled prediction based on historical patterns
  • Reduced manual tracking for thousands of users
Quixel
Epic GamesQuixel
Computer visionImage processingContent generation

Computer vision for AI-assisted content creation

Worked on texture generation systems to improve realism in digital assets by detecting and repairing inconsistencies in mesh-based textures.

QuixelDetection + reconstruction
Artifact detectionRegenerationStitching
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Key capabilities

  • Detection of holes and artifacts in texture meshes
  • Intelligent regeneration of missing regions
  • Context-aware stitching using texture samples

Models and techniques

  • Computer vision algorithms
  • Image pattern matching
  • Texture synthesis and reconstruction

Impact

  • Improved realism of generated assets
  • Reduced manual post-processing effort
  • Enhanced scalability of content pipelines
SkyElectric
SkyElectricSmart Energy ML
Smart energySeasonal forecastingOptimization

Seasonal energy forecasting and optimization

Built ML models using time-based and seasonal electricity usage data to optimize consumption, solar generation, and battery usage.

EnergyForecast + optimize
Demand forecastingSeasonal decompositionBattery strategy
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Key capabilities

  • Electricity consumption forecasting
  • Seasonal pattern detection (summer vs winter)
  • Solar generation prediction
  • Load shedding and backup optimization

Models and techniques

  • Time-series forecasting models
  • Seasonal decomposition of temporal data
  • Regression with time-aware features
  • Optimization models for energy storage

Impact

  • Improved prediction accuracy across seasonal cycles
  • Reduced energy wastage during peak and off-peak periods
  • Enabled smarter energy usage for households
DPL
DPLTalkback
NLPSentiment analysisBehavioral analytics

Sentiment and behavioral analytics at scale

Developed sentiment analysis models to analyze user-generated content related to social causes and charitable activities, with time-based engagement trend insights.

TalkbackTrends + insights
EmbeddingsSupervised learningEngagement trends
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Key capabilities

  • Sentiment classification of user messages
  • Engagement and emotional trend analysis over time
  • Behavioral insight generation

Models and techniques

  • NLP-based sentiment classification
  • Text embeddings
  • Supervised learning models

Impact

  • Automated large-scale sentiment analysis
  • Improved understanding of engagement patterns
  • Enabled data-backed insights for social initiatives
Convo
ConvoConversational Intelligence
Conversation analyticsIntent + topicsUser behavior

Understanding user's sentiment

Applied ML to time-ordered chat data to identify intent, feature usage, and emerging needs, helping product teams prioritize improvements.

ConvoIntent + insights
Intent classificationTopic modelingClustering
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Key capabilities

  • Feature usage analysis over time
  • User intent and behavior discovery
  • Feedback and trend identification

Models and techniques

  • NLP-based text classification
  • Topic modeling
  • Behavioral clustering across time windows

Impact

  • Helped product teams prioritize features
  • Improved user experience through data-driven insights
Social

LinkedIn

Contact

Get in touch

Email

qadeer.qureshi@digitalmiles.org

Location

Pakistan

One-liner

Director of Engineering focused on applied AI and predictive systems using time-series and behavioral data.

What I’m open to

AI product engineering, forecasting systems, decision support.