I process millions of records, build streaming infrastructure, and design AI-driven analytics systems that turn raw events into production intelligence. Currently doing that at Pycube Inc.
Streaming architectures, real-time anomaly detection, container orchestration, and building ML systems that hold up under production load.
Systems thinking over siloed models. Measurable impact over impressive demos. Clean abstractions and honest engineering tradeoffs.
Roles where I can own end-to-end systems — from data ingestion through ML inference to the dashboard someone makes a decision from.
From enterprise healthcare AI to telecom-scale ML — building systems that drive measurable business outcomes.
Deep expertise across AI Engineering, ML/AI, and cloud infrastructure.
End-to-end systems built from scratch — production-grade architecture, real-time pipelines, and ML-powered intelligence.
A full observability platform that simulates infrastructure events, streams them through Kafka, detects anomalies with ML, and displays everything on a live dashboard — updated every 2 seconds via WebSocket.
12 simulated services producing CPU, memory, disk, and network metrics at 1 event/sec each.
Apache Kafka ingestion layer handling high-throughput event streaming with topic partitioning.
4-strategy detection (threshold, z-score, rate-of-change, Isolation Forest) with configurable alerting.
WebSocket-powered real-time UI with Chart.js visualizations updating every 2 seconds.
A ground-up simulator of Kubernetes-style container scheduling and orchestration. Implements core concepts: pod scheduling, node affinity, resource allocation, and health-based eviction — without requiring a full K8s cluster.
Bin-packing and resource-aware placement algorithms assigning pods to nodes by capacity.
Simulated cluster with per-node CPU/memory tracking, availability states, and drain support.
Periodic health checks with automatic restart and rescheduling of failed containers.
Full pod lifecycle from pending through running to terminated with eviction policies.
An intelligent agentic system that reads hospital staff emails, queries asset databases via MCP servers, searches SOPs and user manuals through a RAG pipeline, and replies with accurate, human-like answers — fully automated, end-to-end.
Stateful async graph with conditional routing, parallel MCP tool calls, and a validation gate before every reply.
3 independent servers (MySQL, ChromaDB, Outlook) — agent declares what it needs, never touches infra directly.
PDFs ingested via pdfplumber + GPT-4o OCR, chunked and stored in ChromaDB. AI-generated SOPs auto-indexed per device type.
SQL read-only enforcement, whitelist-only email processing, quality validation, and human-safe fallback on every failure path.
Retrieval-Augmented Generation system with vector indexing, semantic search, and LLM-powered Q&A over custom document corpora.
High-throughput distributed rate limiter using token bucket and sliding window algorithms for multi-node API gateway deployments.
End-to-end pipeline ingesting live infrastructure metrics, applying forecasting models, and surfacing insights for proactive capacity planning.
Open to AI Engineering, Software Engineering, and ML Engineering roles. Feel free to reach out — always happy to chat about systems, data, or interesting problems.