Vinay Gajavelli

AI & GenAI Engineer with almost 5 years of end-to-end experience building production-grade machine learning, LLM, and multi-agent systems across banking, healthcare, and enterprise environments, with a proven ability to take solutions from prototype to scalable deployment. Highly skilled in multi-agent AI architecture, leveraging LangGraph, AutoGen, CrewAl, and LangChain to design coordinated agent ecosystems capable of task delegation, contextual reasoning, and dynamic decision-making with optional human-in-the-loop controls. Deep expertise in Retrieval-Augmented Generation (RAG), including vector database design, embedding pipelines, and retrieval optimization using FAISS, Pinecone, Weaviate, OpenSearch, and ChromaDB to enable low-latency, context-aware LLM responses. Experienced in customizing and optimizing LLM behavior, implementing reinforcement learning techniques such as RLHF and RLAIF to refine model reasoning, improve alignment with business expectations, and enable multi-step problem-solving across diverse tasks. Hands-on experience building context-aware agents, integrating external knowledge sources through LlamaIndex, Neo4j, and enterprise data connectors, and developing rich user interactions through React-based agent interfaces. Strong background in MLOps, DevOps, and scalable deployment, using Docker, Kubernetes, Azure DevOps, GitHub Actions, MLflow, DVC, and Terraform to manage CI/CD pipelines, experiment tracking, reproducibility, and infrastructure automation. Built robust data ingestion and transformation pipelines utilizing Azure Databricks, PySpark, Azure Data Factory, AWS Glue, EMR, Lambda, and GCP DataStream enabling near real-time processing, distributed compute, and high-performance ETL for AI workloads. Developed full-stack machine learning systems using scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, and Hugging Face Transformers, supporting use cases across NLP, classification, time-series forecasting, and pattern recognition. Designed and deployed intelligent automation solutions that integrate AI agents with cloud platforms such as Azure ML, Databricks, AppSheet, and Power Automate, enabling automated triage, workflow routing, decisioning, and system troubleshooting. Strong foundation in data visualization, analytics, and business intelligence, creating impactful dashboards with Power BI, Plotly, Matplotlib, Seaborn, and QuickSight, and using advanced statistical techniques (NumPy, Statsmodels, Prophet) for insights and forecasting. Committed to responsible and explainable AI, applying SHAP, LIME, model fairness checks, drift monitoring, observability tools (CloudWatch, Azure Monitor, Grafana), and strong governance practices to ensure transparency, stability, and trustworthiness of ML and GenAI systems.