Gen AI Engineer - Austin, TX, Houston, TX, San Antonio, TX, Dallas, TX.

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
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