Dinesh Kumar Reddy

Intelligent Automation engineer @ PNC

About

With 12+ years of experience, I specialize in designing, developing, and supporting cloud-native automation and AI agent workflows — using Python, RPA, and LLM orchestration frameworks (LangChain/LangGraph) deployed on AWS with containerized architectures across Financial Services, Healthcare, Government, and Telecom domains. My work focuses on building intelligent automation solutions that replace manual processes, integrate with enterprise systems, and operate reliably at production scale. I have hands-on experience building multi-agent state machines with conditional routing, tool calling, memory management, and guardrails — delivering enterprise-grade automation pipelines adopted by engineering and operations teams at scale. Leveraging expertise in Generative AI, LLMOps, and backend development, I build scalable solutions using Amazon Bedrock, LangChain, LangGraph, FastAPI, Docker, and AWS (Lambda, ECS/Fargate, CloudWatch). I also build React frontend dashboards and Node.js APIs that connect automation outputs to enterprise application consumers. I collaborate closely with onshore architects and senior engineers, following defined architecture standards and delivering automation solutions aligned to enterprise security and governance requirements.

Country

City

United States

Industry

Computer Software

Skill

Statistical Data Analysis, pandas, SQL, NumPy, LangChain, Program Creation, Large Language Models (LLM), MLOps, Microsoft Azure Machine Learning, Azure Kubernetes Service (AKS), OpenAI API, Data Science, Python (Programming Language), Microsoft Power BI

Experience

Intelligent Automation engineer

PNC

2023-7 - Present · 3 yrs 3 mos

Georgia, United States

Developed automation solutions using Python-based RPA — scripting Selenium and Playwright browser automations, task scheduling, document processing, and data extraction workflows integrated with enterprise financial systems via REST APIs and databases. Built AI agents using LangChain and LangGraph to orchestrate tools, APIs, and workflows — multi-agent state machines with conditional routing, tool calling, memory management, and guardrails for reliable enterprise automation pipelines adopted by 80+ financial stakeholders. Built RAG-powered financial knowledge retrieval using Amazon Bedrock, LangChain, and Amazon OpenSearch, reducing analytics turnaround time by 60%. Containerized Python automation and AI agent services using Docker and docker-compose — ECR image management, ECS/Fargate task definitions, and CI/CD pipelines with GitHub Actions and Azure DevOps for automated build, test, and deployment. Deployed and operated automation solutions on AWS — IAM roles, S3 event-triggered workflows, Lambda Python functions, ECS/Fargate containerized services, and CloudWatch structured logging, metrics, and alarms. Built React frontend dashboards and Node.js backend REST APIs — automation monitoring UIs and integration layers connecting AI agent outputs to enterprise application consumers. Implemented logging, error handling, and monitoring — Python structured logging, CloudWatch Log Insights, Dead Letter Queue error handling, retry logic, and alerting for automation reliability and rapid incident response.

AI/ML Automation Engineer

Verizon

2020-12 - 2023-6 · 2 yrs 7 mos

New York, United States

Designed and deployed scalable AI/LLM solutions for chatbots, speech, search, and document processing, improving accuracy, efficiency, and customer experience at scale.