Karachi, Pakistan — est. 2003

Nabeel Mukhtar

Distinguished Software Engineer. 20+ years building enterprise systems, 10+ years shipping production ML — now extending into RAG pipelines, agent orchestration, and LLM observability.

View the career graphSkills diagram

Selected work

Three systems, three tradeoffs

The engagements behind the timeline below — what each needed, what I decided, and what it cost.

Elastica Inc. · Principal Data Scientist · Aug 2013 – Aug 2014

Shipped behavioral anomaly detection for a cloud security platform with zero labeled incidents to train on.

Problem

Elastica CloudSOC needed to flag anomalous user-session behavior for security review, but cloud security anomalies are rare — no labeled dataset of confirmed incidents existed, and waiting to accumulate one would have delayed the feature by quarters.

Approach

Built the anomaly-detection ML workflow end to end: feature extraction from session, geography, categorical, and work-habit logs; K-means outlier detection; rule-based detection via Drools for known policy violations; and exploratory clustering models (1-Class Classification, ORCLUS, DBSCAN, LOF) to validate the approach. Added sensitivity analysis for outlier features, bootstrapped synthetic data to stress-test it, and investigated SVM and deep-learning approaches as the dataset grew.

Unsupervised outlier detection over a trained classifier: Shipped anomaly detection immediately with zero labeled incidents, and let analyst triage become the mechanism that generated the labels a supervised model would eventually need.

Result: Higher false-positive rate than a trained classifier could eventually achieve, but detection shipped immediately — and security analysts triaging flagged sessions generated the labeled incident data a future supervised model would need.

K-meansDBSCANLOFDroolsFeature engineering

Creative Chaos · Distinguished Software Engineer · Apr 2015 – Aug 2018

Built the backend and data-flow engine for Podium Data, an enterprise big-data management platform spanning four Hadoop distributions plus single-server deployments.

Problem

Podium Data needed to run identical pipeline logic across Cloudera, BigInsights, MapR, and Hortonworks clusters as well as single-server deployments, and its data-flow authoring tool needed to recommend next steps to engineers — with no usage history yet to train a recommender on and too small a user base at launch to bootstrap one quickly.

Approach

Wrote the Data Flow Engine running on MapReduce, Spark, and Tez; the multi-target Publish module (FTP/SFTP/S3/HDFS; Parquet/Avro/ORC/Text); and the Data Conductor module for metadata-catalog governance. Built a rule-based recommendation engine encoding known-good pipeline patterns for data-flow authoring, plus custom KPI scoring for managed entities.

Leadership: Later carried this same data-platform expertise into a standing seat on Qlik's Architecture Council (Aug 2018 – Feb 2023), reviewing cross-product microservice standards and driving incident investigations and postmortems with the SRE team as Principal Software Engineer on the Catalog & Cognitive Data Services team.

Rule-based recommendation over ML: With no usage history to train on, a deterministic rule engine beat an uncalibrated model — and bought time to collect the data a future model would need.

Result: Recommendations were only as good as the rules explicitly written, with no ability to surface patterns nobody had thought to encode — a gap that's now the target for an LLM-powered, RAG-based suggestion system at Xymbia, the same underlying need revisited a decade later with a stack that didn't exist yet.

MapReduceSparkTezParquet/Avro/ORCHDFS/S3/SFTP

Xymbia Ventures Inc · Principal Software Engineer · Apr 2023 – Present

Architecting a calendar/productivity SaaS with real-time collaboration and the ML/AI integration layer now extending it into an agentic scheduling assistant.

Problem

Xymbia Planner needed workspace collaboration with real-time event streams (SSE) across NestJS microservices on AWS EKS and Spring Boot microservices for Calendar and Search, plus structured observability as the platform scales — and now a natural-language scheduling assistant that needs similarity search and multi-step reasoning without adding new stateful infrastructure.

Approach

Leading the backend and ML/AI integration layer: NestJS microservices on AWS EKS with MongoDB, Spring Boot microservices for Calendar and Search, Grafana/Loki and Prometheus observability evolving toward OpenTelemetry-based distributed tracing. The in-progress calendar assistant adds pgvector similarity search on the existing Postgres instance and an explicit LangGraph state graph for multi-step scheduling — conflict detection, availability checks, and an interrupt node that pauses for user confirmation before writing back to the calendar.

pgvector over a managed vector DB: Reusing the existing Postgres instance avoids a third stateful dependency for a workload well under a million vectors — revisit only if scale demands a dedicated index.

LangGraph over CrewAI for the scheduling agent: An explicit state graph makes every branch and interrupt point testable in isolation — which matters when the agent is allowed to write to a calendar.

Result: The SaaS platform and its observability stack are in production; the RAG-based scheduling assistant is in progress, with the state-graph design already validated as testable in isolation branch by branch before it's allowed to touch a real calendar.

NestJSSpring BootAWS EKSMongoDBPostgreSQL / pgvectorLangGraphSSEOpenTelemetry

Decision Log

Twenty years of tradeoffs

Architecture is choosing what to give up. A few decisions from the timeline above, and what they cost.

Expertise

Skills, grounded in the work

Skills grouped by category, each tied to the engagement it was built on. Dashed coral chips are 2025–2026 roadmap targets — actively being added on top of the existing depth.

LLM & Agentic Stack

RAG PipelinesVector DBs (pgvector/Chroma/Weaviate)Agent Orchestration (LangGraph)MCPLLM Observability (LangSmith/PostHog)LLM Eval Design (RAGAS/DeepEval)Prompt EngineeringLLM Fine-Tuning (unsloth) & RLHFLLM Serving (vLLM/AWS Bedrock)

Evidence: Xymbia Planner's pgvector + LangGraph scheduling assistant (in progress).

ML / AI (Production)

PyTorchKerasscikit-learnHugging Face TransformersspaCyAnomaly DetectionNLP / NERRecommender Systems

Evidence: Elastica CloudSOC's unsupervised anomaly-detection pipeline.

MLOps & Data Engineering

AWS SageMakerAWS BedrockvLLMMLflowKubernetesDockerCI/CD for ML (GitHub Actions)

Evidence: Xymbia's Grafana/Loki + Prometheus observability stack.

Cloud & Infrastructure

AWS (EKS, EC2, Lambda, SQS, SNS)AzureOpenTelemetryGrafana / PrometheusTerraform

Evidence: AWS EKS deployment at Xymbia; Azure at earlier roles.

Backend & Architecture

JavaGoPythonNestJS / Node.jsSpring BootMicroservicesEvent-Driven ArchitectureREST / GraphQL / gRPCPostgreSQL / MongoDB / Elasticsearch / Neo4j / Redis

Evidence: Podium Data's multi-engine platform; Qlik's cloud-native microservices.

Experience

Twenty years, one throughline

  1. Apr 2023 – Present

    Principal Software Engineer

    Xymbia Ventures Inc

    Architecting Xymbia Planner, a calendar/productivity SaaS with workspace collaboration and real-time event streams (SSE). Leading the backend and ML/AI integration layer — NestJS microservices on AWS EKS, MongoDB, and Spring Boot microservices for Calendar and Search. Implementing structured observability (Grafana/Loki, Prometheus), evolving toward OpenTelemetry-based distributed tracing.

    Extending Xymbia with a RAG-based calendar assistant — vector embeddings, pgvector similarity search, LangGraph agent for multi-step scheduling.

  2. Aug 2018 – Feb 2023

    Principal Software Engineer

    Qlik

    Architect on the Catalog & Cognitive Data Services team, with a standing seat on the Qlik Architecture Council (QAC). Built and unit-tested cloud-native microservices in Spring Boot/Go on Kubernetes, monitored stage/production performance metrics, and drove incident investigations and postmortems with the SRE team.

  3. Apr 2015 – Aug 2018

    Distinguished Software Engineer

    Creative Chaos

    Built the backend for Podium Data, a big-data management platform spanning Cloudera, BigInsights, MapR, and Hortonworks (plus single-server deployments), and its Data Flow Engine running on MapReduce, Spark, and Tez. Wrote the multi-target Publish module (FTP/SFTP/S3/HDFS; Parquet/Avro/ORC/Text) and the Data Conductor module for metadata-catalog governance. Built a rule-based recommendation engine for data-flow authoring and custom KPI scoring for managed entities.

    Replacing the rule-based recommendation engine with an LLM-powered suggestion system using RAG over the metadata catalog — the same pattern now applied to Xymbia.

  4. Aug 2014 – Apr 2015

    Senior Consultant — Architecture & Machine Learning

    GFK Etilize Inc.

    Architected the search and indexing engine for products, with full-text search over rich content (documents and images) via Elasticsearch. Researched NER for product attribute extraction and sentiment analysis for reviews, implemented kNN product classification with Elasticsearch MLT, built the ETL pipeline into a multi-tenant Elasticsearch store, and exposed a REST/HATEOAS search API via Spring REST.

  5. Aug 2013 – Aug 2014

    Principal Data Scientist

    Elastica Inc.

    Built the anomaly-detection ML workflow for Elastica CloudSOC, a cloud security platform — feature extraction from session/geography/categorical/work-habit logs, K-means outlier detection, rule-based detection via Drools, and exploratory clustering models (1-Class Classification, ORCLUS, DBSCAN, LOF). Added sensitivity analysis for outlier features, bootstrapped synthetic data, and investigated SVM/deep-learning approaches.

  6. Apr 2008 – Aug 2013

    Software Architect

    Creative Chaos

    Built the anomaly-detection engine for Compuware Outage Analyzer on streaming data and architected the analytics/reporting platform for Bradford Networks. For BEZNext, built the model service for workload characterization, data-collection and database-advisor modules for SQL Server, and an auto-discovery module for VMware and a workload-management module for Teradata. Architected the face2face backend and its social-network-analysis services on a graph database.

  7. Oct 2003 – Apr 2008

    Senior Software Engineer

    GFK Etilize Inc.

    Built the taxonomy editor for the ConQuire CMS, its multi-market/multilingual module, a cross-sell/up-sell product-similarity recommendation engine, and the data-synchronization module for geographically dispersed servers.

Illustrative workflow

How a query moves through the pipeline

A calendar query moving through Xymbia's RAG pipeline. Timings are illustrative, not measured production numbers.

React Flow

05 / Continuous Calibration

Still calibrating

The skills above didn't stop accruing at the last job change. A running log of coursework layered on top of production experience, newest first.

  • 06:33:12[COMPLETED]Building Multimodal Data PipelinesLLM & Agentic Stack · DeepLearning.AI · Apr 2026
  • 11:27:48[COMPLETED]Agent Memory: Building Memory-Aware AgentsLLM & Agentic Stack · DeepLearning.AI · Mar 2026
  • 03:52:16[COMPLETED]Build and Train an LLM with JAXML / AI (Production) · DeepLearning.AI · Mar 2026
  • 19:14:03[COMPLETED]Agentic Knowledge Graph ConstructionLLM & Agentic Stack · DeepLearning.AI · Aug 2025
  • 08:47:29[COMPLETED]Semantic Caching for AI AgentsLLM & Agentic Stack · DeepLearning.AI · Nov 2025
  • 14:36:55[COMPLETED]Claude Code: A Highly Agentic Coding AssistantLLM & Agentic Stack · DeepLearning.AI · Aug 2025
  • 05:19:41[COMPLETED]Orchestrating Workflows for GenAI ApplicationsMLOps & Data Engineering · DeepLearning.AI · Jun 2025
  • 17:02:38[COMPLETED]MCP: Build Rich-Context AI Apps with AnthropicLLM & Agentic Stack · DeepLearning.AI · May 2025
  • 09:24:17[COMPLETED]How Transformer LLMs WorkML / AI (Production) · DeepLearning.AI · Feb 2025
  • 21:08:52[COMPLETED]Long-Term Agentic Memory With LangGraphLLM & Agentic Stack · DeepLearning.AI · Mar 2025
  • 12:45:09[COMPLETED]AI Agents in LangGraphLLM & Agentic Stack · DeepLearning.AI · Jun 2024
  • 16:31:24[COMPLETED]ChatGPT Prompt Engineering for DevelopersLLM & Agentic Stack · DeepLearning.AI · Apr 2023
  • 10:58:37[COMPLETED]LangChain for LLM Application DevelopmentLLM & Agentic Stack · DeepLearning.AI · May 2023
  • 23:07:14[COMPLETED]How Diffusion Models WorkML / AI (Production) · DeepLearning.AI · May 2023
  • 07:41:52[COMPLETED]Pair Programming with a Large Language ModelLLM & Agentic Stack · DeepLearning.AI · Sep 2023

Recommendations

What it's like to work with

I have worked with Nabeel for 5 years now, and he is simply one of just a handful of technical implementors that I would put in a "top 1%" for quality of code, thoughtfulness with new compelling feature sets and timeliness in deliver. You are luck if you work with someone with one of these attributes, but Nabeel possesses all 3.
Bob Vecchione— Colleague, Qlik
Nabeel is a skilful and focused resource always found to be referred to as highly self-motivated and well capable of achieving goals in timely manner. Displaying a high degree of integrity, responsibility and ambition, Nabeel undoubtedly would be an asset to any organization; I wish him success in all his future endeavours.
Sharmeen Malik, MBA, PHR— Senior Talent Acquisition Advisor, Canada Life
During my work as VP of Development at BEZ, Nabeel was part of the off-shore development team working on BEZNext product and reported directly to me. Nabeel has all the ingredients of an exceptional software developer: eagerness to learn new stuff, creativity, hard work and passion about his work. He was highly respected within the team and people looked up to him whenever a complex problem was encountered. I would welcome the opportunity to work with Nabeel again and highly recommend him as a software developer and architect.
Atif Majid— VP of Development, BEZ
Nabeel is a very gifted software engineer. He does not come across as very communicative, but has excellent listening and writing skills. The best way to see the value he brings would be to give him a problem to be solved and you're guaranteed to be very pleasantly surprised.
Aamir Baig— Co-Founder & CEO, Article.com
Excellent architect with a great attitude.
Asif Sharif— CEO & Founder, Modern Requirements
I found Mr. Nabeel very focused, hard working and committed to his work. His listening and learning skills were very much appreciated. He has great technical knowledge. He had kind of pure software engineer attitude at that time who could work for really long hours in one sit.
Umar Zubair— Fractional CTO, Creative Chaos

06 / Contact

Building something that needs
this kind of depth?

nabeelmukhtar@gmail.comlinkedin.com/in/nabeelmukhtarKarachi, PakistanView resume (PDF)