Note: The job is a remote job and is open to candidates in USA. Dice is seeking a hands-on AI Engineer to design, build, and deploy production-ready Generative AI solutions and intelligent agents. The role focuses on developing AI agents from scratch, building RAG architectures and Python-based services, and implementing MLOps, containerization, and enterprise integrations.
Responsibilities
- Design, develop, and deploy **Generative AI and agentic AI applications** on Google Cloud Platform
- Build intelligent **AI agents from the ground up**, including orchestration, tools, workflows, memory/context, and external system integrations
- Develop applications leveraging **Gemini and other LLMs**
- Design and optimize **RAG architectures** for enterprise data and knowledge retrieval
- Build scalable AI services and APIs using **Python**
- Develop LLM workflows and agent frameworks using **LangChain**
- Implement MLOps practices for model/application deployment, monitoring, versioning, and lifecycle management
- Containerize and deploy AI workloads using **Docker and Kubernetes**
- Integrate AI solutions with **NoSQL databases, APIs, and enterprise systems**
- Evaluate and optimize LLM performance, response quality, latency, reliability, and scalability
- Work in a highly **hands-on engineering capacity**, contributing directly to architecture, coding, testing, deployment, and troubleshooting
Skills
- The ideal candidate is a hands-on engineer who can take AI solutions from concept and prototype through deployment, scaling, and production support
- The engineer must be able to independently design, code, troubleshoot, and productionize complex AI solutions
- Google Cloud Platform (Google Cloud Platform) - strong hands-on experience building and deploying AI/ML solutions in Google Cloud Platform
- Generative AI and Large Language Models (LLMs) - hands-on development experience with production use cases
- Google Gemini - experience working with Gemini models, including Gemini Flash and Gemini model versions
- AI Agent Development - demonstrated experience designing and building AI agents from scratch, including agent workflows, tool integration, orchestration, and reasoning
- Retrieval-Augmented Generation (RAG) - experience designing and implementing production RAG pipelines
- Python - strong hands-on development skills
- LangChain - experience building LLM applications, agents, chains, and integrations
- MLOps - experience deploying, monitoring, maintaining, and scaling AI/ML models and applications
- Docker & Kubernetes - containerization and orchestration experience for production AI workloads
- NoSQL Databases - hands-on experience integrating NoSQL data stores with AI applications
Benefits
- Medical coverage, for those that meet eligibility
- Dental coverage, for those that meet eligibility
- Vision coverage, for those that meet eligibility
- Federal and state leave programs as required by applicable agency regulations, for those that meet eligibility
Company Overview
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