About
Highly accomplished Generative AI & MLOps Engineer with over 3 years of experience in designing and deploying enterprise-grade AI/ML systems. Proven expertise in LLM optimization, RAG pipelines, and scalable MLOps platforms, consistently reducing inference latency and infrastructure costs while boosting model reliability across telecom, SaaS, and manufacturing sectors. Drives business-impacting AI solutions by effectively bridging research and production.
Work
Jersey City, NJ, US
→
Summary
Contributed to AI/ML initiatives at Verizon, focusing on infrastructure optimization, model performance, and anomaly detection.
Highlights
Achieved a 25% reduction in infrastructure spend by implementing serverless LLM APIs on AWS Lambda and SageMaker.
Enhanced model throughput by optimizing transformers with prompt caching and PyTorch Lightning, improving processing efficiency.
Developed and deployed LSTM-based anomaly detectors using AutoGen, decreasing false positives by 45%.
Engineered high-accuracy churn prediction models (XGBoost) with 82% accuracy, contributing to an 18% reduction in system downtime.
Jersey City, NJ, US
→
Summary
Leads the development and deployment of advanced Generative AI and MLOps solutions, optimizing system performance and enhancing customer experience for Verizon.
Highlights
Developed and deployed a multi-agent RAG platform (CrewAI, LangChain), cutting repeat customer tickets by 35% and significantly enhancing overall customer satisfaction.
Optimized large language models by fine-tuning Llama-2 (LoRA/QLoRA) with adaptive RAG, resulting in a 40% reduction in AI hallucination rates.
Architected and delivered a robust voice-enabled AI system utilizing LangGraph, achieving 99.2% uptime and supporting over 10 million monthly interactions.
Reduced cloud inference costs by 35% through strategic migration to AWS Inferentia and deployment of highly efficient serverless LLM APIs.
→
Summary
Designed and implemented machine learning solutions for safety monitoring and predictive maintenance, leveraging computer vision and IoT analytics at CORTracker360.
Highlights
Deployed advanced computer vision safety models (OpenCV, TensorFlow), leading to a 22% reduction in workplace incidents.
Constructed an IoT predictive maintenance pipeline using Spark and Python, achieving 78% accuracy in forecasting equipment failures.
Pioneered the implementation of edge AI on Jetson Nano and Raspberry Pi, enabling low-latency IoT analytics for real-time insights.
→
Summary
Supported the development and optimization of ML pipelines and anomaly detection systems as an ML Engineer Intern at CORTracker360.
Highlights
Developed robust ML pipelines using scikit-learn and Python for real-time workforce safety monitoring.
Reduced model training time by 25% through the application of advanced feature engineering techniques.
Implemented unsupervised anomaly detection and NLP compliance workflows, enhancing operational efficiency by 20%.
Prototyped machine learning models with TensorFlow and PyTorch, accelerating deployment cycles by 30%.
Skills
Generative AI & LLMs
GPT-4, Llama-2, Claude, Mistral, Falcon, LangChain, LlamaIndex, CrewAI, AutoGen, Hugging Face, Generative AI, Retrieval-Augmented Generation (RAG), Prompt Engineering, Multi-Agent Orchestration, Model Fine-Tuning, Conversational AI.
MLOps & Cloud Platforms
MLflow, Kubeflow, Weights & Biases, Airflow, GitHub Actions, Docker, Kubernetes, Terraform, CI/CD for AI, Serverless AI Deployment, AWS, GCP, Azure.
Model Optimization
DeepSpeed, FSDP, TensorRT, ONNX Runtime, LoRA, QLoRA, Quantization (INT8, FP16, 4-bit).
Data Systems & Databases
Weaviate, Pinecone, FAISS, PostgreSQL, BigQuery, ElasticSearch, Vector Databases.
Observability & Monitoring
Prometheus, Grafana, OpenTelemetry, Elastic APM, LLM Guardrails.
Programming & Frameworks
Python, PyTorch, TensorFlow, scikit-learn, Pandas, NumPy, FastAPI, Streamlit.
Machine Learning Domains
Speech Analytics, Edge AI, Anomaly Detection, Churn Prediction, Predictive Maintenance, Computer Vision.