Project Lead at Nexus Orbits Pakistan · SaaS and Web Architect · ML Researcher and Developer
I lead delivery at Nexus Orbits Pakistan, architect SaaS and web platforms end to end, and research machine learning for satellite imagery and applied AI.
GitHub contributions
@AdilMunawar · last 12 months
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Badges and learning paths from GitHub, LeetCode, Microsoft, AWS and Google.
GitHub
LeetCode
Microsoft & AWS
Operationalising EU Space for Security
EUSPA · Aug 2026
MATLAB Onramp
MathWorks · Jul 2026
Advanced CloudFormation: Macros
AWS · Jul 2026
Machine Learning with Python
MIT Professional Education · Jul 2026
Computational Probability and Inference
MIT Professional Education · Jun 2026
Web Analytics by Accenture
Accenture / FutureLearn · Apr 2026
Certified Software Engineer
HackerRank · Apr 2026
Agile Foundations
LinkedIn / PMI · Apr 2026
SQL and Relational Databases 101
Cognitive Class / IBM Skills Network · Mar 2026
Claude in Amazon Bedrock
Anthropic · Mar 2026
Chronicle SOAR Developer
Google Cloud Skills Boost · Feb 2026
Building Language Models on AWS
AWS · Dec 2025
Google Ads Apps Certification
Google Cloud Skills Boost · Dec 2025
Azure Cloud Computing
Microsoft · May 2025
Application Modernization with Google Cloud
Google · Mar 2025
LinkedIn Content and Creative Design
LinkedIn · Mar 2025
MLOps with Vertex AI
Google · Feb 2025
Machine Learning Operations for Generative AI
Google · Feb 2025
Advanced Webhook Concepts
Google Cloud
Advanced Performance Measurements
CCAI Frontend Integrations
Google Cloud
Frontend
Backend & data
Machine learning
Cloud & tooling
Field and crop maps from satellite imagery, delivered as models you can run.
Engagement · Fixed-scope model build or monthly retainer
Language-model systems grounded in your own documents and internal APIs.
Engagement · Fixed-scope build or monthly retainer
Web products that put models and maps in front of the people who need them.
Engagement · Fixed-scope build or ongoing product work
Pipelines that turn raw imagery and tables into queryable, monitored data.
Engagement · Fixed-scope pipeline or monthly retainer
Scope first, then short increments you can review.
01
Discovery call
A short call on the problem, the data you already have and what a good result looks like.
02
Scoped proposal
A written scope with deliverables, assumptions and the engagement model, before any work starts.
03
Build in weekly increments
Short cycles with something to review at the end of each, so course corrections stay cheap.
04
Hand-over with docs
Source, environment setup, run instructions and a walkthrough so your team owns the result.
How we turned high resolution imagery of terraced farmland into a topology-clean parcel layer for Zaraat Dost, using an HRNet-W48 boundary model, overlap-stitched tiled inference, watershed instancing and shared-arc vectorisation.
Per parcel crop mapping for Zaraat Dost Private Limited: cloud-masked Sentinel-2 index series resampled onto a regular grid, a bidirectional GRU with attention pooling, and scheduled district-wide inference delivered as a PostGIS layer.
The deployment layer behind the farmland models: ONNX export with a parity gate, a FastAPI service with a warm session and bounded concurrency, scheduled jobs that claim work by idempotency key and write versioned rows to PostGIS, and an MLflow promotion flow that rolls back by moving an alias.
Grounded question answering over a private knowledge base, built so that refusal is a designed outcome: structure aware chunking, hybrid retrieval fused by reciprocal rank, a cross-encoder reranker, verified citations, and an evaluation harness that existed before any prompt was tuned.
How AdiNox parses otpauth QR codes, generates TOTP codes with WebCrypto, keeps secrets in an AES-GCM vault under a PBKDF2 key, and puts a face-api.js check in front of the code list without pretending a face is a key.
Four disagreeing descriptions of the same fields, GPS tracks, scanned cadastral sheets, operator polygons and model boundaries, were georeferenced, conflated with confidence scoring and reviewed by people, then delivered as a versioned parcel layer to the client's GIS and to PostGIS.
AdiGaze turns a batch of mixed-format resumes into structured, ranked candidate records, and it was built to keep finishing the batch when a provider throttles or a file takes minutes to parse.
A field guide to building AI systems that actually finish work: five patterns (structured cognition, parallel workers, self-healing reflexes, vector memory, and an input immune system) drawn from the recruitment engine, RAG pipeline, and MCP server.
How I keep Postgres-backed backends fast and secure on Supabase under serverless load: connection pooling, targeted indexing, edge compute, async webhooks, and Row Level Security, with the tradeoff behind each decision.
01 · Featured
ModelZaraat Dost Private Limited · Agri-Tech
Semantic segmentation of agricultural parcel boundaries from high-resolution satellite imagery of the Kishtwar region, using an HRNet-W48 backbone that keeps full-resolution feature maps so thin field edges survive downsampling. Trained with a boundary-aware loss on digitised reference parcels, with tiled and overlap-stitched inference, morphological cleanup and vectorisation of the boundary raster into GIS-ready polygons.
02 · Featured
ModelPrivate client · Agri-Tech
Per-parcel crop-type mapping from Sentinel-2 time series, built on cloud-masked NDVI and EVI phenology stacks resampled to a regular temporal grid across the growing season. Compares 1D-CNN, LSTM and temporal-attention classifiers on the sequences, then aggregates pixel predictions to parcel level to produce a crop map with per-class confidence.
03 · Featured
ModelPrivate client · LLM Systems
Retrieval-augmented question answering over a private company knowledge base: document parsing and structure-aware chunking, embedding ingestion into pgvector, and hybrid dense plus keyword search with a cross-encoder reranker. Answers are grounded with inline citations to source passages, and an evaluation harness tracks retrieval recall and answer faithfulness across releases.
Private client · Geospatial
End-to-end pipeline that turns raw satellite imagery into clean vector farm parcels: preprocessing and mosaicking, model-based boundary extraction, polygonisation, topology fixes and attribute joins. Includes a human-in-the-loop QA workflow for reviewing and correcting parcels, with outputs delivered as GeoJSON and Shapefiles and stored in PostGIS for downstream analytics.
Private client · Agri-Tech
Multi-date vegetation-index monitoring over digitised parcels, computing NDVI and related indices per field on each clear Sentinel-2 acquisition and flagging significant deviations from the field's seasonal baseline. Change events and time-series charts are exposed through a lightweight dashboard so agronomists can prioritise field visits.
Internal R&D · Geospatial
Pixel-wise cloud and cloud-shadow segmentation for Sentinel-2 scenes, trained on multispectral bands to produce cleaner masks than the standard scene-classification layer for agricultural time-series work. Used as a preprocessing step so that only valid observations enter the phenology stacks that feed the crop classification models.
Private client · Agri-Tech
Gradient-boosted regression model for field-level yield estimation from tabular agronomic records combined with remote-sensing features such as seasonal NDVI statistics and phenology dates. Uses grouped cross-validation by season and region to avoid leakage, with SHAP values used to explain which features drive each prediction.
Internal R&D · Geospatial
Patch-based convolutional classifier for land-cover and crop classes on multispectral satellite tiles, fine-tuned from an ImageNet-pretrained ResNet with the first convolution adapted to additional bands. Handles severe class imbalance with weighted sampling and focal loss, and reports per-class metrics rather than a single accuracy figure.
Private client · Agri-Tech
Recurrent models over per-field vegetation-index sequences that learn the expected seasonal curve and identify key phenological stages such as emergence, peak and senescence. A reconstruction-error variant flags irrigation gaps and other anomalies when a field's trajectory departs from its learned pattern.
“Co-founding Nexus Orbits Pakistan with Adil has been a highlight of my career. After years of collaborating on countless projects, I can attest to his exceptional skill and vision as a web developer and partner.”
Zoya AliWeb Developer & Co-founder12 min readLLM, Inference, GPU
Why decode is bound by memory bandwidth rather than compute, how large the KV cache really gets, what paged attention and continuous batching recover, how speculative decoding speeds up generation without changing the output distribution, and the three numbers to measure before believing any serving benchmark.
Read note14 min readSegmentation, Raster, PyTorch
12 min readVector Search, HNSW, pgvector
13 min readDeep Learning, Loss Functions, Segmentation
12 min readPyTorch, GPU, Training
13 min readMCP, Agents, TypeScript
14 min readPostgreSQL, Supabase, Security
11 min readRAG, Evaluation, LLM
13 min readNext.js, Streaming, TypeScript
12 min readSecurity, Cryptography, TypeScript
5 min readURLScan, Hacking, Pentesting
5 min readWiFi Security, Hacking, Pentesting
5 min readHacking, Game Security, Reverse Engineering