About Me
AI Engineer building multi-agent systems, RAG pipelines, and production-grade LLM applications.
Machine Learning Engineer specializing in Python, SQL, NLP, Retrieval-Augmented Generation (RAG), Agentic AI, and AWS-native ML systems. Experienced in designing, deploying, and optimizing scalable ML pipelines, LLM applications, and production ready backend services. Proven ability to design low-latency, high-reliability cloud-native sub-200 ms systems, reduce manual overhead approximately 40% with cost minimizing.
Engineering Principles
Building AI systems with transparency, fairness, and accountability. Every pipeline includes guardrails against hallucination, bias, and data leakage.
Designing systems that handle sensitive data responsibly — encryption at rest and in transit, GDPR-aware pipelines, and privacy-first architecture decisions.
Shipping systems that degrade gracefully: circuit breakers, retry logic, monitoring dashboards, and alerting. AI that works at 2 AM without intervention.
Technical Skills
AI & Agentic Systems
- Multi-Agent Orchestration (LangGraph, CrewAI) for automated research workflows
- RAG Pipelines (ChromaDB, FAISS) for contextual enterprise search
- LLM Integration (OpenAI, Hugging Face, vLLM) for production-grade applications
- Prompt Engineering & RLHF for robust, hallucination-free outputs
- Model Context Protocol (MCP) and Tool-Augmented Systems
Machine Learning & Data Science
- Predictive Analytics & Time Series Modeling for risk scoring and forecasting
- Computer Vision (YOLOv8, VGG-16) for medical image analysis and object detection
- NLP & Text Mining for large-scale document processing and sentiment analysis
- Model Optimization (Quantization, LoRA/QLoRA) for low-latency inference
- Feature Engineering & Exploratory Data Analysis (EDA)
Cloud & MLOps Infrastructure
- Model Deployment (AWS SageMaker, Azure, GCP) with auto-scaling and failover
- Containerization & Orchestration (Docker, Kubernetes) for robust environments
- CI/CD Pipelines (GitHub Actions, Azure Pipelines) for automated releases
- Observability & Monitoring (Prometheus, Grafana, MLflow) for model drift detection
- Infrastructure-as-Code (Terraform, Bicep) for reproducible deployments
Backend & Data Engineering
- API Development (FastAPI, Django, Flask) for high-throughput backend services
- Database Architecture (PostgreSQL, MongoDB, Redis) for optimized data retrieval
- ETL/ELT Pipelines (PySpark, Airflow, Databricks) for massive data processing
- Event-Driven & Distributed Systems for fault-tolerant asynchronous execution
- System Design focusing on clean code, reliability, and sub-200ms latency