GenAI Engineer

Salary: $65000 - 109000 per year Requirements: We need strong programming experience in Python. We need hands-on experience building REST APIs with FastAPI or Flask. We need practical experience with Google Gemini APIs, Vertex AI, and other enterprise LLM platforms. We need deep expertise in LangChain and LangGraph. We need experience implementing Retrieval-Augmented Generation architectures. We need solid knowledge of Google Cloud Platform services. We need experience with Vertex AI, IAM, Cloud Run, BigQuery, and Google Cloud Storage. We need experience working with vector databases such as Pinecone, Weaviate, Qdrant, Chroma, or Milvus. We need strong SQL and NoSQL database experience. We need experience troubleshooting complex AI pipelines and distributed applications. We need strong problem-solving and communication skills. Preferred: We value Google Cloud Professional Machine Learning Engineer certification. Preferred: We value Google Cloud Professional Cloud Architect certification. Preferred: We value experience with LlamaIndex. Preferred: We value experience with Hugging Face. Preferred: We value experience with React and TypeScript. Preferred: We value knowledge of agentic AI architectures. Preferred: We value experience with MLOps or LLMOps platforms. Responsibilities: We design, build, and deploy generative AI applications powered by Google Gemini and Vertex AI. We create advanced prompt pipelines, RAG solutions, and AI workflows using LangChain. We design and implement stateful multi-agent AI systems with LangGraph. We develop scalable AI solutions using Google Cloud services such as Vertex AI Search, BigQuery, Cloud Run, Cloud Storage, and IAM. We build robust data ingestion pipelines that support multiple document formats. We implement vector search architectures using Vertex AI Vector Search or external vector databases. We optimize LLM performance through prompt engineering, few-shot learning, and PEFT techniques. We define evaluation metrics for LLM accuracy, latency, hallucination detection, and model performance. We apply LLMOps best practices, including observability, scalability, monitoring, and security. We develop REST APIs with FastAPI or Flask to expose AI services. We collaborate with Product Managers, Data Engineers, and Front-End Developers to integrate AI features into enterprise applications. Technologies: AI Architect BigQuery Cloud FastAPI Flask IAM Support LLM Machine Learning NoSQL Python REST React SQL Security TypeScript More: We are hiring a GenAI Engineer for a 12+ month contract opportunity with W2 candidates only; C2C is not accepted. The role is available in Wilmington, DE, Columbus, OH, Chicago, IL, Minneapolis, MN, Detroit, MI, St. Louis, MO, and Philadelphia, PA. We are building next-generation AI solutions within the Google Cloud ecosystem, with a focus on production-grade applications, intelligent multi-agent systems, scalable cloud-native design, and enterprise AI integration. last updated 28 week of 2026