I build production-grade ML and GenAI systems (RAG, fraud detection, MLOps) across AWS and Azure, focused on shipping reliable models that improve real business workflows.

My work spans classical ML, LLM-based systems, data pipelines, and MLOps, with experience delivering solutions in energy, telecommunications, and technology environments—optimizing for latency, quality, and operational efficiency in production.

🎯 Target roles: Data Scientist (Product), ML Engineer, AI Engineer
🎯 Currently focused on:

• Production LLM applications (RAG, agentic systems, evaluation frameworks)
• Scalable ML pipelines and advanced feature engineering
• AI system design for data-intensive products

📍 Location: Raleigh, NC | Open to Remote & Hybrid roles
📌 Status: Actively seeking new opportunities

• 🎓 Master’s in Data Analytics – Northeastern University

• 💼 3.5+ years building production ML systems across consulting and product teams

• 🚀 Deployed 9 production ML pipelines on AWS SageMaker supporting business-critical workflows

• 🏅 Azure Data Scientist Associate

• 🏅 HashiCorp Certified: Terraform Associate

• 📜 IBM Data Science Professional Certificate

• 📜 Google Data Analytics Professional Certificate

These repositories reflect how I design and ship real-world AI systems, not notebook demos.

Production RAG system for customer support over 150K+ product, review, and policy documents.

• LangChain-based retrieval with vector search and source-grounded answers
• Sub-10ms cached responses; cold queries typically under ~1.5s in local benchmarks
• Evaluation harness with 15+ domain-specific test queries
• Dockerized FastAPI backend with Streamlit UI for interactive exploration

Why this matters: Demonstrates how to build reliable, cost-aware RAG systems that can augment or replace human support workflows while remaining debuggable and transparent.

🔗 View Repository: https://github.com/pranshu1921/shopassist-rag
(Includes architecture diagram and reproducible benchmarks.)

Enterprise-style RAG system using agentic reasoning over custom knowledge bases.

• LangGraph orchestration for multi-step reasoning and tool selection
• FAISS vector store for efficient semantic search
• ReAct-style agent with deterministic source citation
• Modular ingestion for URLs and local documents (PDF/TXT/DOCX)

Why this matters: Mirrors how modern AI teams build trustworthy internal copilots and knowledge systems that can be inspected, debugged, and extended.

🔗 View Repository: https://github.com/pranshu1921/Agentic-RAG-Document-Search-System
(Includes architecture overview and evaluation hooks.)

End-to-end ML system emphasizing maintainability and production correctness.

• Data ingestion → feature engineering → training → evaluation → inference
• Clear separation of data, training, and serving layers
• Reproducible experiments with versioning and automated tests
• Dockerized inference service with CI/CD-ready structure

Why this matters: Shows how to take a model from notebook to production in a way teams can operate, extend, and trust.

🔗 View Repository: https://github.com/pranshu1921/ml-production-pipeline

Business-first ML case studies focused on decisions, not just accuracy.

• Churn modeling with cost-benefit thresholding and retention strategies
• Fraud detection under class imbalance with precision-recall tradeoffs
• End-to-end pipelines from raw data to actionable recommendations

Why this matters: Demonstrates how to translate ambiguous business problems into ML systems that drive real decisions.

🔗 View Repository: https://github.com/pranshu1921/applied-ml-case-studies

Applied Machine Learning
Designing predictive models and experiments that solve concrete business problems, from churn and fraud to operational forecasting.

Generative AI & LLM Systems
Building production RAG and agentic workflows with an emphasis on evaluation, reliability, and source-grounded answers.

Production ML Systems
Taking models from notebook to production through deployment, monitoring, CI/CD, and performance tuning.

Data Engineering for ML
Designing SQL-first pipelines, feature stores, and data quality checks that make ML systems reliable at scale.

I gravitate toward work where models become real systems that teams can trust and operate in production.

• Classical ML: Scikit-learn (custom transformers, pipeline optimization), XGBoost, LightGBM
• Deep Learning: PyTorch (NLP, neural models), TensorFlow
• LLM Systems: LangChain, LangGraph, OpenAI API, prompt engineering
• Vector Search: FAISS, ChromaDB, embeddings (OpenAI, HuggingFace)
• Evaluation: Custom metrics, RAGAS framework, A/B testing

• Languages: Python, SQL (complex queries, optimization), R
• Libraries: Pandas, NumPy, Polars
• Feature Engineering: Domain-specific transformations, time series features
• Data Quality: Great Expectations, custom validation frameworks

• Cloud: AWS (SageMaker, Glue, Lambda, S3, Athena), Azure (OpenAI, AI Search, DevOps)
• Orchestration: Apache Airflow, AWS Step Functions
• Infrastructure as Code: Terraform (multi-account setups, state management)
• Containerization: Docker, Docker Compose
• CI/CD: GitHub Actions, automated testing
• Monitoring: Model performance tracking, data drift detection

• Tools: Tableau, Power BI, Streamlit
• Libraries: Matplotlib, Seaborn, Plotly

I believe strong AI engineers:

• Start with problem framing, not models
• Treat data pipelines as first-class systems
• Optimize for maintainability over cleverness
• Measure success using business impact
• Build systems that explain themselves
• Plan explicitly for failure modes

• Advanced RAG architectures (hybrid search, re-ranking, query decomposition)
• LLM evaluation frameworks (RAGAS, domain-specific metrics)
• Fine-tuning and adapting open-source models (Llama 3, Mistral)
• Production monitoring for LLM systems (cost, latency, hallucination detection)

Seeking:

• Data Scientist, ML Engineer, and AI Engineer roles (Remote or Hybrid)
• Contract or consulting projects in AI/ML system design

Happy to discuss:

• RAG system architecture and evaluation
• MLOps best practices and scaling challenges
• Bridging the gap between research and production

Reach me at:

• LinkedIn: https://www.linkedin.com/in/pranshu-kumar
• Email: pranshukumarpremi@gmail.com

⭐ If you find something useful here, feel free to star a repo or reach out.