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M.S. Data Science — WPI '25

Hi, I'm Sheroz
Shaikh

ML Engineer & Data Scientist building production AI systems that deliver measurable business impact. From deploying LLM-powered medical coding systems serving 10+ enterprise clients to building semantic search platforms saving $80K/month — I turn complex data into intelligent, scalable solutions.

ML Engineer • Data Engineer • AI Systems Builder • Computer Vision
View My Work Get In Touch Download Resume
0+
Years Experience
0K+
Monthly Requests
$0K
/mo Savings Delivered
0.9
GPA @ WPI
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Featured Systems

Production ML Systems

Real-world AI systems I've built and deployed at scale.

LLM Medical Coding System
→ Processes 100K+ monthly requests
→ Used by 10+ enterprise healthcare clients

Semantic Search Platform
→ Indexed 940K healthcare documents
→ Delivered $80K/month operational savings

Document Triage ML Pipeline
→ Processes 900+ documents/day
→ Automated 80% of workflows

About Me

Building Intelligent Systems

From research to production — I bridge the gap between ML models and real-world business value.

sheroz@ml-studio ~ %
>>> import sheroz
>>> sheroz.about()
{
  "name": "Sheroz Shaikh",
  "role": "ML Engineer",
  "education": "M.S. Data Sci @ WPI",
  "gpa": 3.9,
  "focus": [
    "Production LLM Systems",
    "Healthcare AI",
    "Data Engineering",
    "Semantic Search & RAG"
  ],
  "superpower": "Research → Production"
}
>>>

From Models to Business Value

I'm an ML Engineer with 5+ years of experience shipping production AI systems in healthcare and enterprise domains. With an M.S. in Data Science from WPI (3.9 GPA), I specialize in building LLM-powered applications, semantic search platforms, and scalable data pipelines that deliver measurable ROI.

At CrowdANALYTIX, I built a semantic search platform over 940K healthcare documents that saved $80K/month in operational costs. I deploy models that serve 100K+ monthly requests with production-grade monitoring, validation, and error handling.

Production LLM Systems
Healthcare AI / NLP
Semantic Search & RAG
Data Pipeline Design
Time-Series Forecasting
ML Observability
🏆 Best Data Science Project (Winter 2024) — Won 1st place for a healthcare project at WPI, recognized for technical rigor and real-world impact.
Experience

Where I've Shipped AI

Building production ML systems that drive real business outcomes and operational savings.

Biological Information OS, Inc
Machine Learning Engineer
Remote, Florida
Dec 2025 – Present
Python LLMs Neo4j Knowledge Graphs Document Parsing Information Extraction
  • Building a document intelligence pipeline to process large volumes of life science and biomedical publications (textbooks, research papers, and reference material), extracting structured biological entities, relationships, and concepts from unstructured scientific literature
  • Developing document parsing and information extraction workflows that convert scientific publications into structured datasets used for downstream biological knowledge modeling
  • Designing the architecture for a Neo4j-based biological knowledge graph representing entities, citations, ontologies, and relationships across life science domains
  • Preparing the foundation for LLM-powered semantic search and reasoning over structured biological knowledge derived from large-scale scientific literature ingestion
Biogas Engineering
Machine Learning Engineer
Remote, California
May 2025 – Nov 2025
PyTorchFastAPIDockerAWSPrometheusClaude/OpenAI
  • Architected and deployed a containerized ML inference service (FastAPI + Docker) for automated document triage, processing 900+ daily documents with validation pipelines that automated 80% of workflows, reducing support workload by 40% and improving first-contact resolution by 68%
  • Built an LLM-powered routing service using the Claude API to automate ticket categorization and prioritization, integrating model inference into support workflows and automating 40% of classification tasks, eliminating ~$700/month in manual operational costs
  • Deployed a sensor-based time-series forecasting pipeline in PyTorch to predict equipment failures up to 30 days in advance, enabling proactive maintenance scheduling and reducing unplanned downtime
  • Implemented Prometheus-based observability for ML APIs and inference pipelines, tracking system health, latency, and performance metrics with real-time alerting to maintain 99%+ uptime across production document-processing systems
ProsperOn Graduate Consulting
Data Engineer & Analytics
Remote, Boston
Feb 2025 – Apr 2025
PythonSQLAWS S3PolarsPlotlyPostHog
  • Engineered a normalized analytics warehouse from fragmented PostHog event data, enabling SQL-based cohort and retention pipelines that reduced reporting latency by 75% and standardized analytics across product teams
  • Developed automated Plotly dashboards for real-time user adoption and engagement metrics, enabling a self-service analytics model and eliminating recurring ad-hoc analysis requests
Discern Health Graduate Consulting
ML Engineer
Remote, Texas
Aug 2024 – Dec 2024
PySparkSageMakerH2O AutoMLScikit-learnSQL
  • Optimized a production PySpark ETL pipeline processing 15M+ Medicare records by implementing a pre-joined, partitioned event/claims architecture, reducing repeated scans by 75%, cutting storage footprint by 42%, and improving query performance by 58%
  • Improved clinical prediction model recall by 23% through H2O AutoML-driven feature engineering, applying iterative validation loops to identify high-impact predictors for patient outcome classification
CrowdANALYTIX
Senior Machine Learning Engineer
Bangalore, India
May 2018 – Jul 2023
PythonLLMsPyTorchAirflowFastAPISQL
  • Deployed and maintained a production LLM inference system for automated ICD-10 medical coding, serving 10+ enterprise healthcare clients and processing 100K+ monthly requests with integrated validation, monitoring, and error-handling pipelines
  • Built a production semantic search pipeline using vector embeddings across 940K healthcare documents, automating large-scale retrieval workflows that eliminated manual review of 500K+ documents monthly and delivered ~$80K/month in operational savings
  • Designed and automated Airflow DAG-based ETL workflows processing 2M+ monthly records, reducing manual data operations by 70% and saving ~$2.5K/month through scheduled, monitored, and failure-resilient pipeline execution
  • Led end-to-end ML system development from requirements through production deployment, and mentored 3 junior engineers on system design, debugging practices, and scalable ML architecture
Tech Stack

My ML Toolkit

The frameworks, tools, and platforms I use to build and ship production AI systems.

AI & Agentic Frameworks
Transformers LLMs (OpenAI, Anthropic, OpenRouter, Qwen) LLM agents PEFT (LoRA) RAG Systems Prompt Engineering NLP FAISS Chroma Pinecone Qdrant PyTorch Scikit-learn XGBoost H2O AutoML LangChain Semantic Search Vector Search Embeddings Vector DBs Redis vLLM
Data Engineering & ETL
PySpark Polars Apache Airflow (DAG pipelines) SQL Data Pipelines Data Quality Schema Optimization AWS S3 SageMaker Airflow ETL Feature Engineering
Production Systems
FastAPI Docker AWS EC2 Lambda MLflow GitHub Actions CI/CD Prometheus Grafana API Design MLOps DVC ML/data Pipelines Workflow Automation Batch & Scheduled Pipelines Pipeline Monitoring Metrics Tracking & Alerting
Languages & Tools
Python SQL LangChain LangGraph Hugging Face Git Linux Plotly PostHog OCR ONNX Triton Ray Kafka
Research Projects

Research & Open Source

Contributions to the ML community through research and published tools.

Parameter-Efficient Fine-Tuning (LoRA) for Text Classification
Built an end-to-end text classification pipeline with LoRA fine-tuning, using a controlled experimental setup with reproducible preprocessing, training, and benchmarking. Demonstrated efficient adaptation of large language models for domain-specific tasks.
PyTorchLoRA/PEFTTransformersNLP
Open-Source Python Packages (PyPI)
Published 4 production-grade Python packages for ML pipeline profiling, structured logging, and data transformation workflows, enabling teams to standardize monitoring and debugging across ML systems. Available on PyPI for community use.
PythonPyPIML OpsOpen Source
Education

Academic Foundation

M.S. Data Science
Worcester Polytechnic Institute (WPI)
Worcester, MA May 2025 GPA: 3.9 / 4.0
Best Data Science Project (Winter 2024) — 1st place for healthcare project
B.E. Electronics & Telecommunication
AIKTC School of Engineering and Technology
Mumbai, India May 2016
Contact

Let's Build Intelligent Systems Together

Whether it's deploying LLMs at scale, building data pipelines, or discussing the future of AI in healthcare — let's connect.

shaikh.sheroz07@gmail.com +1 (781) 655-9092 sherozshaikh sherozshaikh PyPI
Send Me a Message