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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Fisher Investments - **Location:** Plano, TX, United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Apache HTTP Server, Automated Storage and Retrieval Systems, Automation of Tests, Microsoft Azure, Cloud Engineering, Nvidia CUDA, Distributed Computing Environment, Graph Database, Information Retrieval, Python (Programming Language), Machine Learning, Language Modeling, Neo4j, Software Engineering, Software Systems, Systems Integration, AI Infrastructure, Graphics Processing Unit (GPU), Enterprise Software Applications, Data Ingestion, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Software Application Programming, Generative AI, Agentic-AI, Fastapi, Data Lakes, AI Platforms, Kubernetes, Information Technology, Low Latency, Data Analytics, Cosmos DB, Machine Learning Operations, VLLM, Docker, Databricks, Microservices - **Published:** October 30, 2026 - **Apply:** https://www.careerbuilder.com/job-details/ai-engineer-plano-tx--40274dd0-3820-446f-b1c8-f11924fd5252 ## About the Role Artificial Intelligence (AI), Software Engineering, Application Programming Interface (API), GPU (Graphics Processing Unit), Artificial Intelligence (AI), Modeling Languages, Microservices, Team Lead/Manager, Microsoft Windows Azure, System Integration (SI), * Minimum of 6+ years of experience building data-driven software solutions * Minimum of 3+ years of hands-on AI development experience. * Proficiency in Python, with practical experience building applications using LLMs, embeddings, vector databases, and RAG architecture. * Demonstrated experience with generative AI models, including multimodal models. * Hands-on experience developing and deploying AI applications using Azure Foundry, Docker, Kubernetes, and GPU-backed compute. * Bachelor's degree in computer science, Artificial Intelligence, or a related field, or equivalent professional experience * Experience fine-tuning, evaluating, and deploying models using Azure-managed AI services and compute resources * Familiarity with agent frameworks and experience deploying AI agents with tool integration, appropriate permissions, and operational safeguards * Experience building graph-enhanced RAG and hybrid retrieval solutions using graph databases such as Neo4j, Stardog, or Azure Cosmos DB for Apache Gremlin * Experience deploying models with low latency and high throughput, using frameworks like vLLM and other GPU model deployment tools * Hands-on experience building CI/CD pipelines and automated testing workflows for generative AI application * Experience with distributed training, GPU optimization, and large-scale model frameworks (CUDA, RAPIDS) ## Description It is an exciting time to join Fisher Investments! We are expanding our Generative AI capabilities to support our international growth. We are looking for a Software Engineer who will design, build, and improve advanced AI applications using large language models, GPU-accelerated computing, and modern API frameworks., The Software Engineer will architect, implement, and improve production-ready generative AI systems, working closely with AI leads, ML engineers, and platform teams. You will develop solutions powered by LLMs, context engineering, and GPU-accelerated frameworks, using Azure, Databricks, graph databases, and scalable microservices to deliver high-performance, secure applications integrated with enterprise systems. You will report to the AI Team Leader, Technology Innovation. The Day-to-Day: * Design, develop, fine-tune, and deploy generative AI models into scalable production environments * Build and maintain APIs and microservices using FastAPI to expose AI capabilities enterprise wide * Collaborate with the AI Infrastructure team to architect robust LLM pipelines, including training workflows and retrieval-augmented generation (RAG) systems * Integrate AI solutions into enterprise applications using secure, cloud-native architectures and best practices * Build and operate data ingestion, transformation, and orchestration pipelines using Databricks, Delta Lake, Auto Loader, and Workflows to support AI applications and retrieval systems * Integrate graph databases into AI applications to model relationships and support graph-enhanced RAG and hybrid retrieval solutions * Ensure AI models are explainable, reliable, and compliant with regulatory and internal governance standards * Monitor and improve application and model performance using evaluation frameworks, observability tools, and iterative improvements to prompts, retrieval, context, and model configuration