GenAI Engineer
QTech•Chicago, MI
•Onsite
About The Position
We are looking for an experienced GenAI Engineer to design and build next-generation AI applications using Google Gemini, Vertex AI, and the Google Cloud Platform (GCP) ecosystem. The ideal candidate will have strong expertise in LangChain, LangGraph, Retrieval-Augmented Generation (RAG), agentic AI workflows, and scalable cloud-native architectures. This role involves building production-grade AI solutions, integrating LLMs into enterprise applications, and developing intelligent multi-agent systems.
Requirements
- Strong programming experience in Python.
- Experience building REST APIs using FastAPI or Flask.
- Hands-on experience with Google Gemini APIs, Vertex AI, and other enterprise LLM platforms.
- Strong expertise with Langchain and LangGraph.
- Experience implementing RAG architecture.
- Strong knowledge of Google Cloud Platform (GCP).
- Experience with Vertex AI, IAM, Cloud Run, BigQuery, and Google Cloud Storage.
- Experience working with Vector Databases including Pinecone, Weaviate, Qdrant, Chroma, or Milvus.
- Strong SQL and NoSQL database experience.
- Experience debugging complex AI pipelines and distributed applications.
- Strong problem-solving and communication skills.
Nice To Haves
- Google Cloud Professional Machine Learning Engineer Certification.
- Google Cloud Professional Cloud Architect Certification.
- Experience with Llama Index.
- Experience with Hugging Face.
- Experience with React and TypeScript.
- Knowledge of Agentic AI architectures.
- Experience with MLOps or LLMOps platforms.
Responsibilities
- Design, develop, and deploy Generative AI applications powered by Google Gemini (Pro, Flash, Ultra) and Vertex AI.
- Build advanced prompt pipelines, RAG applications, and AI workflows using LangChain.
- Design and implement stateful, multi-agent AI systems using LangGraph.
- Develop scalable AI solutions utilizing Google Cloud services including Vertex AI Search, BigQuery, Cloud Run, Cloud Storage, and IAM.
- Build robust data ingestion pipelines supporting multiple document formats.
- Implement vector search architectures using Vertex AI Vector Search or vector databases such as Chroma, Milvus, Pinecone, Weaviate, or Qdrant.
- Optimize LLM performance using prompt engineering, few-shot learning, and PEFT techniques.
- Establish evaluation metrics for LLM accuracy, latency, hallucination detection, and model performance.
- Implement LLMOps best practices including observability, scalability, monitoring, and security.
- Develop REST APIs using FastAPI or Flask to expose AI services.
- Collaborate with Product Managers, Data Engineers, and Front-End Developers to integrate AI capabilities into enterprise applications.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
