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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software/AI Engineer - **Company:** SOFTWARE CONSULTANTS INC. - **Location:** United States (Remote available) - **Contract:** Temporary contract - **Skills:** Java (Programming Language), JavaScript (Programming Language), .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, C Sharp (Programming Language), Cloud Computing, Cloud Database, Cloud Engineering, Computer Programming, Databases, Continuous Integration, Data Cleansing, Information Engineering, DevOps, Python (Programming Language), Open Source Technology, Cloud Services, Search Technologies, Software Deployment, Systems Integration, TypeScript, Unstructured Data, Data Logging, Google Cloud, Enterprise Software Applications, Data Ingestion, Large Language Models, Prompt Engineering, Model Validation, Generative AI, Backend, Production Code, Data Management, Api Design, GPT, Data Pipelines, Automation Anywhere, Serverless Computing, Programming Languages - **Published:** September 23, 2026 - **Apply:** https://www.dice.com/job-detail/edbe043a-42c8-4719-a1de-ad0e1593796a ## About the Role The ideal candidate will have practical experience building and deploying AI/GenAI solutions on AWS or Google Cloud Platform, with Azure experience being a plus. The candidate should be comfortable working with LLMs, RAG architectures, AI frameworks, data pipelines, APIs, and production-grade cloud applications. This is a hands-on engineering role requiring strong programming skills and the ability to design, develop, integrate, test, troubleshoot, and deploy AI-powered solutions., Programming Languages Strong hands-on programming experience with one or more of the following: * Python strongly preferred for AI/GenAI and data engineering * C# / .NET * Java * JavaScript * TypeScript Candidates should be able to write production-quality code and should have strong software engineering fundamentals. Cloud & Engineering * Strong hands-on experience with AWS or Google Cloud Platform. * Experience developing and deploying applications/services in a cloud environment. * Experience with cloud-native architectures and services. * Azure experience is a plus. * Experience developing APIs and backend services. * Experience with databases and distributed/cloud-based systems. * Understanding of containers, serverless technologies, CI/CD, and DevOps practices is preferred. Generative AI / LLM Strong practical experience with Generative AI and Large Language Models, including one or more of: * OpenAI / GPT models * Meta Llama * Google Gemini * Open-source LLMs * LLM APIs and model integration * Prompt engineering * Embeddings * Vector search * Model evaluation and optimization RAG & AI Application Development Hands-on experience building RAG-based applications, including: * Document ingestion * Data preprocessing and chunking * Embeddings * Vector databases / vector search * Semantic retrieval * Context construction * Prompt orchestration * Response generation * RAG evaluation and optimization Experience with: * LangChain * LlamaIndex * LangGraph * Other equivalent GenAI frameworks ## Description We are looking for a strong experience in cloud-based data and AI engineering, Generative AI, Large Language Models (LLMs), RAG architectures, and AI application development., * Design, develop, and deploy cloud-based data and AI solutions using AWS or Google Cloud Platform. * Build scalable and production-ready applications using Python and other modern programming languages. * Develop and integrate Generative AI and LLM-powered applications. * Work with commercial and open-source LLMs such as: * OpenAI / GPT * Meta Llama * Google Gemini * Other open-source LLMs Build and implement Retrieval-Augmented Generation (RAG) solutions. Design RAG pipelines including document ingestion, preprocessing, chunking, embeddings, retrieval, reranking, context management, and response generation. Work with LangChain, LlamaIndex, LangGraph, and similar GenAI frameworks. Develop AI agents and graph-based AI workflows where applicable. Build integrations between LLMs, enterprise applications, APIs, databases, data platforms, and cloud services. Develop data ingestion and transformation pipelines supporting AI/ML and GenAI applications. Work with both structured and unstructured data. Implement vector search, embeddings, and vector database solutions. Build APIs and backend services to expose AI capabilities to enterprise applications. Implement monitoring, logging, testing, evaluation, and observability for AI/LLM applications. Optimize AI applications for performance, scalability, reliability, security, and cost. Troubleshoot production issues and continuously improve AI and data engineering solutions. Collaborate with architects, data engineers, software engineers, product teams, and business stakeholders to translate requirements into technical solutions. ## Related Videos - [Should we build Generative AI into our existing software?](https://www.wearedevelopers.com/videos/1129-should-we-build-generative-ai-into-our-existing-software) - [Livecoding with AI](https://www.wearedevelopers.com/videos/1201-livecoding-with-ai) - [Shifting Stress to Progress— Understanding DevOps to do DevOps Better](https://www.wearedevelopers.com/videos/268-shifting-stress-to-progress-understanding-devops-to-do-devops-better) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Mobile vs. Backend DevOps](https://www.wearedevelopers.com/videos/1662-mobile-vs-backend-devops) - [Exploring LLMs across clouds](https://www.wearedevelopers.com/videos/1457-exploring-llms-across-clouds) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Got AI ideas but no money? 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