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NOTE: This position is fully remote and follows Central Time (CST) working hours.
AI Engineer – Generative AI / Agentic AI
Position Summary
Seeking a hands-on AI Engineer to design, build, and deploy production-ready Generative AI solutions and intelligent agents. This role requires strong experience with Google Cloud Platform (GCP), Gemini/LLMs, RAG, Python, and MLOps, with a particular focus on building AI agents from scratch rather than simply integrating pre-built solutions.
Ideal Candidate
The ideal candidate is a hands-on engineer who can take AI solutions from concept and prototype through deployment, scaling, and production support. This role is not suited to candidates whose experience is primarily theoretical, managerial, or limited to using existing AI tools. The engineer must be able to independently design, code, troubleshoot, and productionize complex AI solutions.
Must-Have Skills
- Google Cloud Platform (GCP) – strong hands-on experience building and deploying AI/ML solutions in GCP
- 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
Key Responsibilities
- Design, develop, and deploy Generative AI and agentic AI applications on GCP
- 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
Interested candidates may submit their resumes online or call at 310-906-4780 for further information regarding the position.
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