AI/ML Engineer · Data Scientist · Python Developer MS Data Science @ Saint Peter's University · TechRxiv preprint author · KCD New York 2026 Speaker · Based in New York

I build AI/ML systems that make it from research to production — and I work the full stack of that journey: framing the problem as a data scientist, training the model as an ML engineer, and shipping it as reliable Python software that survives production.

Most engineers pick a lane — research, modeling, or infrastructure. I work the whole line: multi-agent systems, RAG, and LLM pipelines engineered onto Kubernetes, with the data and evaluation infrastructure to keep them honest.

My focus areas:

• 🤖 Agentic AI & LLMs — autonomous agents, multi-agent orchestration, RAG, fine-tuning, vLLM serving
• 🧠 ML & Data Science — forecasting, anomaly detection, predictive modeling, rigorous evaluation
• 🐍 Python software & MLOps — FastAPI services, event-driven microservices, CI/CD, observability on Kubernetes
• 🔒 Applied ML — production cybersecurity and edge-AI systems

📄 Published research: Democratizing long-context LLMs on edge devices — IEEE TechRxiv, Jan 2026.

• KCD New York 2026 — Scaling Production RAG Systems with Kubernetes · June 10, 2026 · (demo repo · watch the talk)
• Apache Beam Summit New York 2026 — Real-Time AI Pipelines at Scale: Embedding LLMs into Apache Beam · June 22, 2026 · (demo & slides)

Aug 2023 – Jul 2024 · Mumbai, India

• Deployed FastAPI backend services on Kubernetes via CI/CD pipelines, serving ML predictions at sub-100ms latency while handling ~50K daily API calls
• Engineered a Kafka-based data pipeline integrating external security APIs — managing rate limits, retries, and normalization to process ~500K daily events
• Integrated a Llama-based LLM into the automated code-review workflow, with structured logging and tracing to monitor model decisions and detect regressions

Jan 2021 – Jul 2023 · Mumbai, India

• Built a Python NLP pipeline classifying support tickets and automating routing, cutting manual triage time ~40%
• Trained churn-prediction models on SQL-extracted usage data, helping retention teams flag at-risk accounts and reduce monthly attrition ~25%
• Ran A/B testing infrastructure for targeted campaigns, driving a ~32% improvement in conversion rates

Languages

Python • SQL • C++ • Java • TypeScript • Rust • Go

AI/ML & LLMs

PyTorch • TensorFlow • Transformers • LangChain • LangGraph • LlamaIndex • CrewAI
RAG • Fine-Tuning • Quantization (GGUF/GPTQ) • LoRA/QLoRA • vLLM • DSPy • Guardrails.ai
Multi-Agent Systems • Memory-Augmented Agents • Tool-Use Planning

Data Science & Analytics

scikit-learn • XGBoost • Pandas • NumPy • Time-Series Forecasting • Anomaly Detection
A/B Testing • NLP • Computer Vision

MLOps & Infrastructure

Docker • Kubernetes • Ray Serve • Ollama • MLflow • Terraform • CI/CD • Model Monitoring

Vector Search

FAISS • Pinecone • ChromaDB • Elasticsearch • Hybrid Search • Embeddings

Data Engineering & Cloud

Apache Spark • Apache Beam • Airflow • Snowflake • ETL Pipelines
AWS (Bedrock, SageMaker) • GCP Vertex AI • Azure OpenAI • REST APIs • Microservices

Kotlin · LLaMA · GGUF · RAG — On-device LLM inference with zero cloud dependency. Runs LLaMA 3.1, IBM Granite, and Mistral locally via 4-bit GGUF quantization at ~8 tokens/sec, with on-device RAG over your documents.

Python · Research · State-Space — Reference code for my TechRxiv preprint: hierarchical latent state-space recurrence that democratizes long-context LLMs on edge devices, as a transformer alternative.

Python · Kubernetes · vLLM · RAG — Demo from my KCD New York 2026 talk: deploying, autoscaling, and observing production RAG pipelines on Kubernetes with vLLM and vector search.

Python · Apache Beam · LLM — Real-time AI pipelines at scale: embedding LLMs and RAG directly into Apache Beam transforms for low-latency inference on high-velocity data streams.

🔗 More on GitHub: GateKeep (agentic finance tracker) · AIORNOR (AI-vs-human text detector) · see everything (17 repos)

🎓 MS, Data Science — Saint Peter's University · New Jersey · Sep 2024 – May 2026 · GPA 3.9/4.0 🎓 BS, Data Science — University of Mumbai · 2020 – 2023 · GPA 3.95/4.0

"HLST: Democratizing Long-Context LLMs on Edge Devices via Hierarchical Latent State-Space Recurrence" IEEE TechRxiv, January 2026 · DOI: 10.36227/techrxiv.176948384.49791660/v1

I write about production AI on Medium — recent pieces on scaling RAG, the state of retrieval, and agent skills.

Open to full-time roles in AI/ML Engineering, Data Science, and Python development, plus research collaborations and consulting. Based in New York, shipping worldwide.

🌐 samcodeman.com · 💼 LinkedIn · 🐙 GitHub · ✍️ Medium · 🐦 X

"Building intelligent systems that make it to production — and stay there."