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GenAI Engineer

QTech US IncNew York, NY🇺🇸United StatesPosted 20 Jul 2026

Why This Role Stands Out

This hybrid GenAI Engineer role offers a fantastic opportunity to build cutting-edge AI applications using Google's advanced platforms and tools. You'll thrive here if you have expertise in LLMs, LangChain, and cloud-native architectures, and are eager to develop scalable, production-grade AI solutions. Apply now to shape the future of AI with a reputable company!

Quick Overview

Work Type
Hybrid
Level
Mid Senior

Job Description

Job Title: GenAI Engineer
Location: New York City, NY, Boston, MA, Hartford, CT, Princeton, NJ, Philadelphia, PA
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.
Required Skills:
Python
Google Gemini
Vertex AI
Google Cloud Platform (Google Cloud Platform)
Langchain
LangGraph
FastAPI
Flask
RAG
Preferred Skills:
Llama Index
Hugging Face
React
TypeScript
PEFT
LLMOps
Agentic AI
Vertex AI Vector Search
Few Shot Learning
Cloud Architecture
Best Regards,
Teresa Rose

Skills

FastAPI
Flask
SQL
Machine Learning
BigQuery
Generative AI
Google Cloud
Hugging Face
LLM
Python
REST
React
TypeScript

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