Enterprise AI Engineer (GCP)

We are looking for a Principal Enterprise AI Engineer to architect and deliver high-impact AI solutions within the Google Cloud ecosystem. This role is designed for a technical leader who can bridge the gap between complex data landscapes and autonomous AI systems. You will lead the development of Agentic AI frameworks and Data Intelligence platforms that drive significant digital transformation for global enterprise clients. Core Responsibilities

• Architect Agentic Systems: Design and deploy multi-agent orchestration frameworks using Vertex AI Agent Builder, LangGraph, or CrewAI to automate complex, multi-step business workflows.

• Master RAG Architectures: Build and optimize high-performance Retrieval- Augmented Generation (RAG) systems, ensuring LLMs are grounded in enterprise data across BigQuery and Databricks.

• Model Strategy & Optimization: Select and fine-tune models within the Gemini 1.5 family, balancing high-reasoning capabilities (Pro) with high-speed efficiency (Flash) for production-grade latency.

• Legacy Transformation: Lead the strategic migration of legacy analytics logic (e.g., SAS environments) into modern, AI-powered cloud architectures.

• GTM Collaboration: Work closely with Go-To-Market (GTM) leadership to translate technical AI roadmaps into measurable business value for C-suite stakeholders. Required Skill Requirements

1. Agentic AI & Orchestration

• Framework Mastery: Expert implementation of LangChain, LangGraph, or LlamaIndex for stateful, autonomous agent development.

• Advanced Prompting: Proficiency in Chain-of-Thought (CoT), ReAct patterns, and system instruction optimization to ensure reliable model output.

• Function Calling: Experience building custom tools that allow LLMs to interact securely with enterprise APIs and SQL databases.

2. Data Intelligence & Engineering

• Hybrid Data Ecosystems: Deep experience integrating Google Cloud AI services with Databricks (Delta Lake) for unified data intelligence.

• Vector Engineering: Proficiency with Vertex AI Vector Search (formerly Matching Engine) and embedding strategies for large-scale semantic search.

• Data Flow: Skill in building scalable pipelines using Dataflow or Spark to process unstructured data for AI readiness.

3. LLMOps & Production Engineering

• Evaluation Frameworks: Ability to build automated "LLM-as-a-judge" evaluation pipelines to track accuracy, faithfulness, and hallucination rates.

• Cloud Infrastructure: Mastery of the Vertex AI suite (Studio, Model Garden, Pipelines) and Infrastructure as Code (Terraform).

• Programming: Expert-level Python (FastAPI, Pydantic) and advanced SQL.

4. Strategic Governance

• Responsible AI: Implementation of safety filters, PII redaction, and ethical AI monitoring.

• Business Translation: Ability to convert technical metrics (latency, token costs) into business KPIs (ROI, process efficiency).

• Experience: 8+ years in Software Engineering or Data Science, with at least 3+ years focused on production-grade AI/ML.

• Education: B.S./M.S. in Computer Science, AI, or a related quantitative field.

• Certifications: Google Professional Machine Learning Engineer or Professional Cloud Architect (preferred). Technology Stack

• AI/ML: Vertex AI, Gemini 1.5 Pro/Flash, PyTorch.

• Data: BigQuery, Databricks, Vertex Vector Search.

• Orchestration: LangGraph, Vertex AI Agent Builder.

• DevOps: GitHub Actions, Terraform, Vertex AI Pipelines.

• $110.00 per hour Join a dynamic network of Full-Stack Engineers and connect with leading AI labs and companies that are actively seeking your expertise. This opportunity allows you to engage with i…

• 4 days ago

SaidGig + Atlanta, GA + $100.00 per hour Join a dynamic model-training project with a leading foundational model AI lab, where your expertise in Python will play a crucial role in developing complex, production-grade syst… + 4 days ago, © 2026 Careerjet All rights reserved