● Lead the architecture and enterprise deployment of Generative AI solutions for supply chain planning & forecasting using Azure OpenAI, LangChain, LangGraph, and Python, enabling AI driven decision intelligence across mission critical operations.

● Architect multiagent and Agent to Agent (A2A) orchestration frameworks using LangGraph to coordinate retrieval, reasoning, and validation agents for complex, multi step supply chain decision workflows.

● Design and implement scalable Retrieval Augmented Generation (RAG) platforms leveraging Azure Cognitive Search (vector indexing), Azure OpenAI embeddings, Azure Blob Storage, and semantic chunking to power contextual enterprise knowledge retrieval.

● Apply parameter efficient fine tuning techniques (LoRA, QLoRA) using Hugging Face and PyTorch to adapt pretrained LLMs to domain specific supply chain terminology while optimizing compute utilization.

● Develop and govern production grade AI microservices using Python, Go, FastAPI, Azure Functions, and Azure App Service, exposing secure REST APIs consumed by enterprise planning and analytics systems.

● Evaluate and implement vector database strategies (Pinecone, Weaviate) to optimize semantic search performance, relevance scoring, and latency at enterprise scale.

● Establish enterprise data ingestion and transformation pipelines using Azure Data Factory, Databricks (PySpark), Pandas, and Blob Storage to convert ERP and unstructured documentation into vectorized knowledge assets.

● Enhance model reliability and response accuracy by reducing hallucinations by 38% through structured prompt engineering, few-shot design, token optimization, and validation pipelines.

● Enforce enterprise grade security through OAuth2, Azure Entra ID, Managed Identities, Azure API Management, Key Vault, private endpoints, and RBAC controls.

● Operationalized GenAI workloads using Docker, AKS, ACR, Helm, GitHub Actions, and CI/CD with monitoring via Prometheus and Grafana.

● Directed the architecture and deployment of enterprise AI and Generative AI platforms for banking operations using Amazon SageMaker, Bedrock, LangChain, LangGraph, and Hugging Face within regulated financial environments.

● Designed multi-agent orchestration frameworks to coordinate policy retrieval, compliance reasoning, and response validation for complex, audit-sensitive workflows.

● Implemented parameter efficient fine tuning (LoRA, QLoRA) to specialize LLMs for financial and regulatory language while maintaining cost and governance controls.

● Engineered secure Retrieval Augmented Generation architectures using Amazon OpenSearch (vector search), FAISS, S3, and embedding models to enable contextual search across enterprise policy

repositories.

● Developed high-throughput AI microservices using Python, FastAPI, Flask, Docker, and AWS Lambda, supporting millions of monthly inference requests with sub-second latency.

● Integrated Neo4j knowledge graphs to enhance entity-aware retrieval and context reasoning within compliance intelligence systems.

● Built document intelligence pipelines using Amazon Textract and NLP preprocessing workflows, reducing document processing time by 48%.

● Governed AI workloads using EC2, ECS, EKS, ECR, Terraform, and CI/CD pipelines aligned with regulatory standards and change management frameworks.

● Implemented comprehensive monitoring, audit logging, and compliance observability using CloudWatch, CloudTrail, and OpenSearch Dashboards.

● Ensured model risk governance through benchmarking, bias evaluation, human in the loop validation, and UAT cycles in partnership with risk and compliance stakeholders.

● Delivered end to end AI and Generative AI solutions for smart energy analytics platforms, integrating LLM-driven insights, predictive modeling, and real-time IoT processing to support grid operations.

● Designed autonomous agent based workflows using LangChain and CrewAI to orchestrate anomaly detection, contextual data retrieval, and operational insight generation.

● Developed LLM enabled applications using Amazon Bedrock and Hugging Face within RAG architectures to enable natural-language querying of smart-meter, outage, and grid datasets.

● Built forecasting and anomaly detection models using SageMaker, XGBoost, TensorFlow, and time-series feature engineering, improving forecast accuracy by 14% over baseline models.

● Engineered scalable ingestion and processing pipelines using AWS IoT Core, Kinesis, Glue, S3, Spark (EMR), and OpenSearch to support both batch and real-time ML workloads.

● Operationalized AI models using MLOps best practices including SageMaker Pipelines, experiment tracking, automated retraining, and environment promotion.

● Deployed containerized AI services across Kubernetes environments (EKS/ECS/EC2) with CI/CD automation and production-grade monitoring frameworks.

● Developed scalable healthcare web applications using Python (Flask, Django, FastAPI) and built responsive user interfaces with React, Next.js, JavaScript, TypeScript, HTML, and CSS, ensuring seamless frontend-backend integration.

● Designed and implemented RESTful APIs and integrated them with React-based dashboards to enable real-time clinical data visualization, patient monitoring, and analytics reporting.

● Engineered machine learning and deep learning models including CNNs, RNN/LSTMs, GANs, and Transformer-based NLP architectures to generate actionable healthcare insights from structured and unstructured datasets.

● Built and deployed end-to-end ML pipelines using Kubeflow on Google Kubernetes Engine (GKE) and managed scalable model serving through Google Vertex AI.

● Developed computer vision and NLP solutions using OpenCV, YOLO, and Hugging Face (BERT, GPT), integrating model outputs into user-facing applications to enhance diagnostic and patient

communication workflows.

● Leveraged Google BigQuery, Cloud Storage, and Dataflow for large-scale data processing, and implemented MLflow for model lifecycle management, experiment tracking, and reproducibility.

● Designed and developed enterprise microservices using Python (Flask, Django, FastAPI) to support

scalable, distributed backend systems.

● Deployed high availability services using Azure Functions and AKS, implementing containerized and serverless architectures.

● Architected event driven systems using Kafka, Azure Event Hubs, and Service Bus to enable real time,

asynchronous data processing.

● Built and automated CI/CD pipelines using Azure DevOps and GitHub Actions, implementing infrastructure provisioning and application deployments in multi-cloud environments using Terraform and Ansible.

● Optimized database performance across PostgreSQL, MySQL, Cassandra, and Azure Cognitive Search

through indexing and schema improvements.

● Integrated AI services and analytics components into Azure-hosted enterprise applications.