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# Python Full Stack Engineer

- **Company:** Lorven Technologies Inc
- **Location:** Dallas, TX, United States
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Amazon Web Services, Application Performance Management, Automated Storage and Retrieval Systems, Microsoft Azure, Code Review, Distributed Computing Environment, Github, Python (Programming Language), Scrum Methodology, Redis, E2e Testing, Message Oriented Middleware, Software Deployment, Datadog, Enterprise Software Applications, Spring Cloud, Retrieval-Augmented Generation, System Availability, Flask (Web Framework), Large Language Models, Prompt Engineering, Infrastructure as Code (IaC), Cloudformation, Fastapi, Event Driven Architecture, Information Technology, Apache Kafka, Virtual Agents, Terraform, Splunk, Docker, Jenkins, Microservices
- **Published:** June 2, 2026
- **Apply:** https://www.dice.com/job-detail/b73a45ac-484e-45bf-8607-b5c87f2fda13

## About the Role

Required Skills: Python, AWS, LLM, and AI

## Description

* Bachelor's degree in computer science, Information Technology, or a related field
 * Developed and maintained scalable Python-based microservices and REST/gRPC APIs for enterprise applications.
 * Designed and implemented AI-powered solutions using Large Language Models (LLMs), prompt engineering, and agentic AI frameworks.
 * Integrated OpenAI, LangChain, and LangGraph to build intelligent workflow automation and AI agent orchestration systems.
 * Built and deployed cloud-native applications on AWS/Azure using Docker and Kubernetes.
 * Developed backend services using FastAPI/Flask with emphasis on performance, scalability, and security.
 * Implemented CI/CD pipelines using Jenkins, GitHub Actions, and Azure DevOps to automate application deployment.
 * Utilized Terraform/CloudFormation for Infrastructure as Code (IaC) and cloud resource provisioning.
 * Designed event-driven architectures using Kafka and asynchronous messaging patterns.
 * Integrated vector databases and retrieval systems to support RAG (Retrieval-Augmented Generation) applications.
 * Implemented observability and monitoring solutions using Datadog, Splunk, and OpenTelemetry.
 * Developed automated unit, integration, and end-to-end test frameworks to ensure platform reliability.
 * Optimized application performance using Redis caching, asynchronous Python programming, and distributed processing techniques.
 * Collaborated with ML Engineers and Solution Architects to integrate AI models into production environments.
 * Participated in production support, troubleshooting, incident management, and on-call rotations.
 * Followed Agile methodologies, conducting code reviews, sprint planning, and continuous improvement initiatives.


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