Technology

AI Engineer (Generative AI & Google Ecosystem)

Critical River, Inc.San Francisco, CA🇺🇸United StatesPosted Sep 30, 2026

Why This Role Stands Out

This hybrid role offers the chance to build cutting-edge generative AI applications within the Google ecosystem, providing significant opportunities for skill development and career growth. If you're an experienced AI Engineer passionate about leveraging tools like Google Gemini, Vertex AI, LangChain, and LangGraph, this position is an excellent opportunity to make a real impact. Apply now to join a dynamic team and advance your expertise in a flexible work environment.

Quick Overview

Seniority

Mid Senior

Work mode

Hybrid

Location

San Francisco, CA, United States

Posted

2 weeks ago

MLOpsBigQueryData PipelineGenerative AIGoogle CloudLLM

Job Description

Job Title: AI Engineer (Generative AI & Google Ecosystem)
Location: SFO, CA (2-3days Hybrid)
Duration: 6months

Key Responsibilities

Application Development: Design, build, and deploy generative AI applications powered by Google Gemini (Pro, Flash, and Ultra) and Vertex AI.
Orchestration & Workflow Design: Utilize LangChain to build complex prompt pipelines and RAG systems. Design and implement stateful, multi-agent workflows and cyclical AI processes using LangGraph.
Google Cloud Integration: Architect solutions utilizing the Google Cloud Platform (Google Cloud Platform) ecosystem, including Vertex AI Search and Conversation, BigQuery, Cloud Run, and Google Cloud Storage.
Data Pipeline & RAG Engineering: Ingest, process, and chunk diverse data formats. Implement robust vector search architectures using Google Vertex AI Vector Search or open-source vector databases (e.g., Chroma, Milvus).
Model Optimization: Employ advanced prompt engineering techniques, few-shot learning, and parameter-efficient fine-tuning (PEFT) to optimize Gemini''''s performance for specific industry use cases.
Evaluation & MLOps: Establish rigorous evaluation metrics for LLM outputs (accuracy, latency, hallucination rates). Deploy models using modern LLMOps practices to ensure observability, scalability, and security.
Collaboration: Work closely with product managers, data engineers, and front-end developers to seamlessly integrate AI features into our core products.