Job Title: GenAI Engineer
Location: Austin, TX, Houston, TX, San Antonio, TX, Dallas, TX.
Contact: 12+ Months
Looking for W2 candidates. No C2C
Job Summary:
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 (Google Cloud Platform) 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.
Key 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.
Required Qualifications
· 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 (Google Cloud Platform).
· 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.
Preferred Qualifications
· 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.
Required Skills
· Python
· Google Gemini
· Vertex AI
· Google Cloud Platform (Google Cloud Platform)
· Langchain
· LangGraph
· FastAPI
· Flask
· RAG
· Prompt Engineering
· Vector Databases
· Pinecone
· Weaviate
· Qdrant
· Chroma
· Milvus
· BigQuery
· Cloud Run
· Google Cloud Storage
· REST APIs
Preferred Skills
· Llama Index
· Hugging Face
· React
· TypeScript
· PEFT
· LLMOps
· Agentic AI
· Vertex AI Vector Search
· Few Shot Learning
· Cloud Architecture
Best Regards:
Tina
Phone:
Email:
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