• Integrated React + FastAPI apps and a Python forecasting model (Prophet, scikit-learn) to automate inventory workflows, cutting manual tracking 40% and boosting uptime to 99.9%.

• Built an agentic RAG platform (LangChain + Azure OpenAI) integrating NetSuite ERP, supplier APIs, logistics datasets; added RBAC + field-level PII masking, reducing planner reporting time 70% and improving fulfillment 15%.

• Architected a high-concurrency MCP gateway via Celigo AI Studio to expose NetSuite logic as governed LLM tools; reduced retrieval latency 40% using RESTlet lightweight flows + optimized JSON schema mapping.

• Developed an automated web monitoring agent to detect Twisted X listings across retailer URLs using Playwright + LLM classification (Claude API) for product matching, pricing compliance, and false-positive reduction; deployed as a Copilot-style continuous agent.

• Built a multimodal style similarity engine: unified internal/competitor catalogs in DuckDB + embedding-based vector search with multi-signal scoring (image + attribute embeddings) to enable image-upload search, reduce line duplication, and find assortment gaps/competitor equivalents.

• Deployed real-time Python anomaly detection (Isolation Forest, DBSCAN) with Kafka streaming + Pandas/NumPy to flag inventory/order inconsistencies and cut reconciliation 30%.

• Implemented REST + GraphQL APIs for ERP/e-commerce; improved accuracy and production schedules via real-time sync using Node.js, MongoDB, Oracle, combining supplier + logistics APIs.

• Improved React.js performance with Webpack code-splitting + lazy loading (and other optimizations), reducing load time 3.0s → 1.8s.

• Optimized Dockerized deployments and CI/CD (GitHub Actions + AWS EKS) for faster rollouts, zero-downtime updates, and 60% faster environment setup.

• Led TDD + Agile adoption, delivering 95% of milestones on time and reducing post-deployment defects 15%.

• Implemented Google ADK-style agent orchestration inside Cairo-SDK, adding structured plan generation/execution, tool invocation contracts, and first-class artifact lifecycle (create/version/attach/trace) with deterministic replay.

• Built a hybrid router + planner that composes ADK tool schemas with TypeScript decorators/metadata reflection to auto-register tools, infer capabilities, score intents, and dispatch to the right executor with typed inputs/outputs.

• Developed an async summarization + memory pipeline (queue-based) with PII detection/redaction, configurable retention, and AES-256 encrypted at-rest persistence (key rotation-ready), producing signed summaries for downstream retrieval.

• Integrated A2UI as an agent-facing UI layer: streamed step events (plan → tool call → result), rendered artifacts, and exposed human-in-the-loop approvals/edits via stateful checkpoints and resumable execution.

• Implemented multi-agent orchestration with ADK primitives: coordinator + specialist agents (planner/executor/retriever/validator), shared context store, message routing, conflict-safe merges, and recovery flows (timeouts, retries, fallback agent selection).

• Shipped governance + controls for enterprise execution: policy engine for tool allow/deny + per-scope permissions, rate/concurrency limits, sandboxed tool execution boundaries, and immutable audit trails (step-level logs, provenance, and access tracing).

Architected scalable Spring Boot microservices containerized with Docker + AWS EKS, processing 5 M+ events/day with zero downtime using DDD + OpenAPI for enterprise API design.

Led Java 17 / React TypeScript code reviews with OWASP + SonarQube, preventing 30+ critical defects before release and improving overall code quality.

Automated incident remediation via Spring Boot health checks + Kubernetes operators, removing 80% of recurring issues and achieving 99.99% uptime.

Optimized Elasticsearch/OpenSearch + MongoDB clusters (AWS ES Service) indexing 50 M+ docs, boosting query performance by 70% under peak load.

Streamed 1 M+ ingestion events/day through Apache Kafka + Spark Streaming, orchestrating AI enrichment with Kubernetes Jobs for ≤ 200 ms end-to-end latency.

Implemented circuit-breakers, rate-limiting, and JWT/OAuth2 security, enabling 99.99% availability with Prometheus + Grafana monitoring and proactive alerting

• Conducted performance analysis of Digital Twin models for wireless networks, identifying optimization strategies that improved simulated network efficiency by 28%.

• Fine-tuned LLMs using NLP techniques to automate insight extraction and improve predictive accuracy by 15% on wireless communication datasets.

• Programmed scalable data pipelines in Python for processing large-scale network data, leveraging Pandas, NumPy, and Scikit-learn to enhance feature engineering and reduce processing time by 35%.

• Leveraged ML algorithms and deep learning models with TensorFlow and PyTorch to forecast network load and traffic patterns, achieving 92% model accuracy.

• Utilized Jupyter Notebooks and deployed models using AWS SageMaker and GCP Vertex AI, accelerating training workflows and reducing iteration time by 40%.

• Delivered end-to-end Java/J2EE solutions on Oracle and MySQL within Agile SCRUM teams, leading requirement analysis, design, and deployment to reduce turnaround by 15% and boost CPU efficiency by 20%.

• Strengthened system throughput and resilience by architecting Spring MVC microservices with multithreading and hash-based caching layers, reducing average API response time from 200 ms to 160 ms under 1,000 RPS peak load.

• Deployed end-to-end ETL pipelines processing over 500 GB/day using Apache Spark for batch jobs, Apache Kafka for streaming ingestion, and HDFS for storage cutting job latency by 30% and delivering 99.9% reliability.

• Developed comprehensive API-driven front-ends, SOAP building RESTful Spring Boot and services consumed by an Angular 8 UI, optimized client-side rendering and minimized page-load times by 25%

• Assessed critical performance indicators using AWS CloudWatch and implemented optimizations that slashed server latency from 1.2 seconds down to an impressive 800 milliseconds under heavy traffic conditions.

• Enhanced Agile workflows with Scrum and Kanban, reducing average sprint cycle time by 30% (from 14 to 10 days) and fostering cross-functional collaboration.