Senior AI/ML & AIOps Engineer at Gigamon

Developed LangChain/LangGraph-based AI agent orchestration to automate network operations workflows, root-cause analysis, and remediation. Built enterprise AI agents using Google Vertex AI Agent Builder with contextual reasoning and enterprise knowledge retrieval. Implemented collaborative multi-agent architectures with Google ADK for planning, task execution, validation, and response generation. Created REST APIs and AI microservices integrating OpenAI/Azure OpenAI, observability systems, and enterprise applications. Integrated enterprise data sources (BigQuery, AlloyDB) via MCP for secure grounding and real-time contextual access. Deployed containerized AI workloads with Docker and Kubernetes across hybrid Azure and GCP. Implemented anomaly detection, predictive analytics, alert correlation, and forecasting using TensorFlow/PyTorch. Designed MLOps/AIOps pipelines for continuous training, deployment, monitoring, and optimization using Azure ML and GCP services, plus CI/CD with Azure D

AI/ML Engineer at NextEra Energy

Mapped business AI workflows for LLM integration. Conducted feasibility for RAG and prompt engineering pipelines. Built modular LangChain/LangGraph workflows for multi-step agent orchestration. Designed secure, scalable RAG systems using Pinecone/Redis vector stores and OpenAI/Hugging Face embeddings, grounded with BigQuery Vector Search. Implemented Vertex AI Endpoints with REST APIs for online prediction. Built secure training/inference knowledge retrieval using GCS and BigQuery with Vertex AI. Developed prompt templates and LLM chaining framework with FastAPI. Fine-tuned GPT-4-based models for summarization, classification, and chatbot interactions. Integrated Neo4j knowledge graphs to improve semantic retrieval. Implemented streaming ingestion to continuously update vector DBs/KBs. Used AWS Bedrock and Azure OpenAI for high-availability low-latency model serving. Added CI/CD using GitHub Actions, AWS CodePipeline, and Jenkins; tracked experiments with MLflow and versioned with DVC;

AI/ML Engineer at AT&T

Built ML models for customer lifetime value (LTV) and current value (ACV). Performed data cleaning, scaling, and feature engineering with pandas and NumPy. Used vector databases (Pinecone, Milvus) for embedding storage and retrieval. Developed LLM-based natural language to MySQL query generation and improved query efficiency by 40% via fine-tuning. Implemented embeddings and NLP techniques including OpenAI embeddings, Word2Vec, GloVe, BERT, and ELMO. Developed predictive algorithms (regression, negative binomial, decision trees, XGBoost). Built LLM training pipelines using resource optimization techniques (mixed precision, gradient checkpointing). Used Generative AI/RAG (LangChain) to match data between MongoDB and SAP, reducing migration time/effort by 70%. Built NLP clustering pipelines and an invoice extraction system using CV/NLP with a Hugging Face-based API and DocQuery achieving 95% accuracy.

AI/ML Engineer at Net Health

Developed and productionized predictive analytics for patient readmission risk to reduce healthcare costs. Built EHR data pipelines with Python/pandas for ingestion, cleaning, and preprocessing. Implemented and deployed Logistic Regression and XGBoost models using TensorFlow/Keras to predict readmission likelihood. Performed feature selection using LASSO and RFE. Deployed model as a REST API on AWS (EC2, S3, ELB) for real-time risk assessment. Used Hugging Face Transformers for NLP extraction of medical conditions and concepts from clinical notes. Established data validation with Pytest; worked with compliance teams for HIPAA-compliant secure data processing using MongoDB. Implemented NLP and LLM-based RAG in Azure for automated data analysis and sentiment improvements. Automated workflows with Alteryx to reduce manual processing time by 35% and improve accuracy by 20%. Built scalable ETL pipelines using Azure Data Factory and Azure Databricks, integrating into ADLS Gen2. Used PySpark

Data Scientist at Walmart

Designed and deployed ML models on AWS SageMaker for classification, regression, and recommendation. Applied regression and multivariate analysis for sales/customer engagement insights and forecasting with sensitivity analysis to support incentive planning. Built NLP/ML-driven recommendations for customer insights systems. Developed production inference services for maintainability and performance. Wrote and optimized SQL, stored procedures, and views in Microsoft SQL Server. Conducted EDA using pandas/numpy/scipy/seaborn/matplotlib/scikit-learn. Applied factor analysis, ANOVA, and hypothesis testing to validate experiments and guide decision-making. Delivered analytical reports via SQL/Python for cross-functional stakeholders and influenced operational strategies.