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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/LLM Engineer - **Company:** NTT - **Location:** Pittsburgh, PA, United States (Remote available) - **Experience:** Expert - **Salary:** $114,400.0 - $128,960.0 - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Cloud Computing Security, Program Optimization, DevOps, Python (Programming Language), Machine Learning, Search Technologies, Software Engineering, Systems Integration, Enterprise Software Applications, Delivery Pipeline, Large Language Models, Multi-Agent Systems, Prompt Engineering, IT Architecture, Model Validation, Software Application Programming, Generative AI, Backend, Event Driven Architecture, Containerization, AI Platforms, Information Technology, Machine Learning Operations, Virtual Agents, Restful APIs, Docker, Microservices - **Published:** August 16, 2026 - **Apply:** https://www.dice.com/job-detail/5c4b2498-8c80-4f38-a003-c76dcd1d34d9 ## About the Role * Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field. * 6+ years of software engineering or machine learning engineering experience. * Minimum 3+ years of hands-on experience building Artificial Intelligence and Generative AI solutions. * 3 to 5 years of strong hands-on experience developing production applications using Python. * 1 to 3 years of experience building applications using OpenAI (preferred), Anthropic Claude, Gemini, Llama, or similar LLM platforms. * 3+ years of strong experience implementing Retrieval-Augmented Generation (RAG) architectures. * 1 to 3 years of experience integrating vector databases and semantic search solutions. * 1 to 3 years of strong understanding of LangChain and AI orchestration frameworks. * Experience designing multi-agent AI architectures. * Strong knowledge of prompt engineering, model evaluation, AI governance, and Responsible AI principles. * Experience building REST APIs, microservices, and event-driven architectures. * Experience with Azure or AWS cloud platforms. * Strong understanding of scalable enterprise application architecture. * Excellent analytical, problem-solving, and communication skills. Required Technical Skills * Python * JavaScript * Generative AI * Large Language Models (LLMs) * OpenAI (Preferred) * Retrieval-Augmented Generation (RAG) * Multi-Agent AI Orchestration * LangChain * Langfuse * AI Solution Architecture * Azure AI Search * Vector Databases * Prompt Engineering * REST APIs * Microservices * Event-Driven Architecture * Azure or AWS Cloud Services Preferred Qualifications * Experience with LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar AI agent frameworks. * Experience with vector databases such as Pinecone, Weaviate, ChromaDB, Qdrant, Milvus, or Azure AI Search. * Experience with containerization technologies such as Docker and Kubernetes. * Knowledge of CI/CD pipelines and MLOps practices. * Experience with AI observability and monitoring platforms. * Familiarity with enterprise security, compliance, and Responsible AI frameworks. * Experience working in Agile/Scrum environments. Nice to Have * Experience developing enterprise copilots or AI assistants. * Experience integrating AI into enterprise SaaS platforms. * Knowledge of AI governance, security, and compliance standards. * Experience optimizing LLM inference performance and AI operational costs. ## Description We are seeking an experienced Senior AI/LLM Engineer to design, develop, and deploy enterprise-grade Generative AI solutions that leverage Large Language Models (LLMs) to solve complex business challenges. The ideal candidate will have strong expertise in Python development, Retrieval-Augmented Generation (RAG), AI orchestration frameworks, and cloud-native AI architectures. This role will work closely with product owners, solution architects, data engineers, and business stakeholders to build scalable, secure, and production-ready AI applications powered by OpenAI and other leading foundation models. Key Responsibilities AI Solution Design & Development * Design, develop, and deploy enterprise-scale Generative AI applications using modern LLM technologies. * Build production-grade backend services using Python and modern software engineering practices. * Develop scalable AI architectures utilizing OpenAI, Anthropic Claude, Gemini, Llama, or similar foundation models. * Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases and semantic search capabilities. * Develop intelligent multi-agent AI systems capable of orchestrating complex business workflows. AI Architecture & Integration * Design AI solution architectures that are scalable, secure, maintainable, and aligned with enterprise standards. * Integrate AI capabilities into existing enterprise applications, APIs, and business workflows. * Develop and consume REST APIs, microservices, and event-driven services for AI applications. * Implement AI orchestration frameworks such as LangChain and related agent frameworks. Prompt Engineering & Model Optimization * Develop and optimize prompts for improved accuracy, reasoning, and business outcomes. * Evaluate LLM performance and implement techniques to improve response quality. * Establish AI governance, model evaluation, and responsible AI best practices. * Monitor AI application performance and continuously optimize latency, cost, and quality. Cloud & Enterprise AI * Build cloud-native AI solutions using Azure or AWS AI services. * Implement Azure AI Search and vector search capabilities. * Design secure enterprise AI applications following cloud security and governance standards. * Collaborate with DevOps teams to deploy AI solutions using CI/CD pipelines. * Stakeholder Collaboration * Partner with business stakeholders to understand AI use cases and translate them into scalable technical solutions. * Present architecture decisions, solution approaches, and AI strategies to both technical and non-technical audiences. * Mentor junior engineers and contribute to AI engineering best practices across the organization. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Building AI Applications with LangChain and Node.js](https://www.wearedevelopers.com/videos/1512-building-ai-applications-with-langchain-and-node-js) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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