# 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. Based in Karachi, Pakistan.

This file summarizes Nabeel Mukhtar's portfolio site for AI assistants. For live, cited answers,
connect to the site's MCP server (Streamable HTTP, no auth): https://nabeelmukhtar.com/api/mcp — tools: get_profile,
get_career_timeline, get_case_studies, get_architecture_decisions, search_background, analyze_job_fit.

## Links
- [Portfolio](https://nabeelmukhtar.com): case studies, decision log, live RAG demo, fit analyzer
- [Résumé (PDF)](https://nabeelmukhtar.com/Nabeel-Mukhtar-resume.pdf)
- [LinkedIn](https://linkedin.com/in/nabeelmukhtar)
- [GitHub](https://github.com/nabeelmukhtar)
- [Kaggle](https://www.kaggle.com/nabeelmukhtar)
- Email: nabeelmukhtar@gmail.com

## Career
- **Principal Software Engineer, Xymbia Ventures Inc** (Apr 2023 – Present): 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. Next: Extending Xymbia with a RAG-based calendar assistant — vector embeddings, pgvector similarity search, LangGraph agent for multi-step scheduling.
- **Principal Software Engineer, Qlik** (Aug 2018 – Feb 2023): 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.
- **Distinguished Software Engineer, Creative Chaos** (Apr 2015 – Aug 2018): 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. Next: 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.
- **Senior Consultant — Architecture & Machine Learning, GFK Etilize Inc.** (Aug 2014 – Apr 2015): 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.
- **Principal Data Scientist, Elastica Inc.** (Aug 2013 – Aug 2014): 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.
- **Software Architect, Creative Chaos** (Apr 2008 – Aug 2013): 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.
- **Senior Software Engineer, GFK Etilize Inc.** (Oct 2003 – Apr 2008): 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.

## Case studies
- **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. 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. Tech: K-means, DBSCAN, LOF, Drools, Feature 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. 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. Tech: MapReduce, Spark, Tez, Parquet/Avro/ORC, HDFS/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. 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. Tech: NestJS, Spring Boot, AWS EKS, MongoDB, PostgreSQL / pgvector, LangGraph, SSE, OpenTelemetry.

## Architecture decisions
- **Rule-based recommendation over ML** (shipped): 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.
- **Unsupervised outlier detection over a trained classifier** (shipped): 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.
- **pgvector over a managed vector DB** (in-progress): 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** (in-progress): 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.

## Skills
- **LLM & Agentic Stack**: RAG Pipelines, Vector DBs (pgvector/Chroma/Weaviate) (in progress), Agent Orchestration (LangGraph) (in progress), MCP (in progress), LLM Observability (LangSmith/PostHog/Langfuse) (in progress), LLM Eval Design (RAGAS/DeepEval) (in progress), Agentic Loop Engineering/Harness Engineering, LLM Fine-Tuning (unsloth) & RLHF (in progress), LLM Serving (vLLM/AWS Bedrock) (in progress). Evidence: Xymbia Planner's pgvector + LangGraph scheduling assistant (in progress).
- **ML / AI (Production)**: PyTorch, Keras, scikit-learn, Hugging Face Transformers, spaCy, Anomaly Detection, NLP / NER, Recommender Systems. Evidence: Elastica CloudSOC's unsupervised anomaly-detection pipeline.
- **MLOps & Data Engineering**: AWS SageMaker (in progress), AWS Bedrock (in progress), vLLM (in progress), MLflow (in progress), Kubernetes, Docker, CI/CD for ML (GitHub Actions). Evidence: Xymbia's Grafana/Loki + Prometheus observability stack.
- **Cloud & Infrastructure**: AWS (EKS, EC2, Lambda, SQS, SNS), Azure, OpenTelemetry, Grafana / Prometheus, Terraform. Evidence: AWS EKS deployment at Xymbia; Azure at earlier roles.
- **Backend & Architecture**: Java, Go, Python, NestJS / Node.js, Spring Boot, Microservices, Event-Driven Architecture, REST / GraphQL / gRPC, PostgreSQL / MongoDB / Elasticsearch / Snowflake / Neo4j / Redis. Evidence: Podium Data's multi-engine platform; Qlik's cloud-native microservices.

## Optional

### Recent coursework
- Building Multimodal Data Pipelines (DeepLearning.AI, Apr 2026)
- Agent Memory: Building Memory-Aware Agents (DeepLearning.AI, Mar 2026)
- Build and Train an LLM with JAX (DeepLearning.AI, Mar 2026)
- Agentic Knowledge Graph Construction (DeepLearning.AI, Aug 2025)
- Semantic Caching for AI Agents (DeepLearning.AI, Nov 2025)
- Orchestrating Workflows for GenAI Applications (DeepLearning.AI, Jun 2025)
- How Transformer LLMs Work (DeepLearning.AI, Feb 2025)
- Long-Term Agentic Memory With LangGraph (DeepLearning.AI, Mar 2025)
- AI Agents in LangGraph (DeepLearning.AI, Jun 2024)
- How Diffusion Models Work (DeepLearning.AI, May 2023)